JP2020523103A - Respiratory volume monitor and ventilator - Google Patents

Abstract

Ventilation therapy systems and methods are disclosed. The system includes a computing device and a plurality of sensors operably connected to the computing device for obtaining a physiological bioelectrical impedance signal from a patient. The computing device receives the physiological bioelectrical impedance signal from the sensor, analyzes the physiological bioelectrical impedance signal, and based on the analyzed physiological bioelectrical impedance signal, before and/or after extubation. The patient's respiratory status is monitored and based on the patient's respiratory status, an audible or visual recommendation for additional respiratory therapy or medication is provided.

Description

This application claims priority to US Provisional Application No. 62/516,425 entitled "Respiratory Volume Monitor and Ventilator", filed June 7, 2017, which is incorporated in its entirety. ..

The present invention is directed to methods and devices for improving ventilation therapy. Specifically, the present invention initiates invasive or non-invasive ventilation therapy for monitoring a patient's respiratory status before and after extubation, and based on patient impedance measurements, or Methods and devices for terminating or adjusting are directed.

Physiological Monitoring-History and Evolution Patient monitoring is essential. The reason is that it provides a warning for patient deterioration, allows for early intervention opportunities and greatly improves patient outcomes. For example, modern monitoring devices can detect abnormalities in heart rhythm, blood oxygen saturation, and body temperature, which can alert clinicians to deterioration that would otherwise go unnoticed. Is.

The earliest record of patient monitoring reveals as early as 1550 BC that the ancient Egyptians knew of the correlation between peripheral vascular pulse and heart beat. Three thousand years have passed before Galileo, which used a pendulum to measure pulse rate, made the next significant advance in monitoring. In 1887, Waller determined that he could passively record electrical activity across the chest by using electrodes, and also correlated the signal with activity from the heart. It was Waller's findings paved the way for the use of electrical signals as a method for measuring physiological signals. However, it still took time for scientists to realize the benefits of monitoring physiological signals in a clinical setting.

In 1925, MacKenzie emphasized the importance of continuous recording and monitoring of physiological signals such as pulse rate and blood pressure. Specifically, he emphasized that the graphical representation of these signals is important in assessing the patient's condition. In the 1960s, with the advent of computers, patient monitors improved with the addition of real-time graphical displays of multiple vital signs being recorded simultaneously. Also, alarms have been incorporated into the monitor and triggered when signals such as pulse rate or blood pressure reach certain thresholds.

The first patient monitor was worn on the patient during surgery. Monitoring of vital signs has spread to other areas of the hospital, such as intensive care units and emergency departments, as patient outcomes have been shown to improve. For example, pulse oximetry was first widely used in the operating room as a non-invasive, continuous method of measuring patient oxygenation. Pulse oximetry rapidly became the standard of care for the administration of general anesthetics, and subsequently spread to other parts of the hospital, including recovery rooms and intensive care units.

The growing need for improved patient monitoring The number of critically ill patients handed to the emergency department is increasing at a high rate, and these patients require close monitoring. Critical care procedures, such as cardiovascular or chest and respiratory procedures (mechanical ventilation, catheterization, arterial cannulation) etc., are performed among 1-8% of patients in the emergency department. It has been estimated that there is a need.

Physiological scores, for example, Mortality Probability Model (MPM), Accue Physiology and Chronic Health Education (APACHE), Simplified Assistance Surgery, and Surgery Surgery Priority SPS (Physiologic Surgery), Surgery Assisted Surgery, and Syringe Assisted Surgery (PSA), Surgery Assisted Surgery, and Surgery Assisted Surgery (PSA). showed that. Monitoring sick patients by using physiological scores and vital signs early in the disease, even before organ failure or shock, improves outcomes. Close monitoring of the patient allows recognition of the patient's degeneration and administration of the appropriate therapy.

However, current scoring methods do not accurately predict patient outcomes in approximately 15% of ICU patients, and it is a respiratory intensive care unit, which cares for a large number of patients with acute respiratory failure in hospitals. Provided, but with respect to the patient in, it can be made worse. Moreover, currently monitored differences in vital signs, such as blood oxygenation, occur late in the progression of respiratory or cardiovascular disorders. Often, the earliest sign of patient deterioration is a change in the patient's respiratory effort or pattern.

Respiration rate is recognized as a vital indicator of patient health and is used to assess patient status. However, respiratory rate alone cannot exhibit significant physiological changes, such as changes in respiratory volume. Metrics derived from continuous volumetric measurements have been shown to have great potential for determining patient status in a wide range of clinical applications. However, currently there is not enough system to be able to accurately and conveniently determine respiratory volume, which requires the need for a non-invasive respiratory monitor that can trace changes in respiratory volume. Motivate.

Disadvantages of Current Methods Currently, patient respiratory status is monitored by such methods as spirometry and end tidal CO ₂ measurements. These methods are often inconvenient to use and inaccurate. End-tidal CO ₂ monitoring is useful in the evaluation of patients intubated in various settings and during anesthesia, but it is inaccurate for non-ventilated patients. Spirometers and pneumotachometers are limited in their measurement and are highly dependent on patient effort and proper coaching by the clinician. Effective training and quality assurance are necessary for successful spirometry. However, these two prerequisites are not necessarily enforced in clinical practice, such as when they are in research laboratories and lung function laboratories. Therefore, quality assurance is essential to prevent leading to wrong results.

Virometry is the most commonly performed lung function test. Spirometers and pneumotachometers can provide a direct measurement of respiratory volume. It involves assessing a patient's breathing pattern by measuring the volume or flow of air as it enters and leaves the patient's body. The spirometry procedure and procedure has been standardized by the American Thoracic Society (ATS) and the European Respiratory Society (ERS). Virometry can provide important metrics for assessing respiratory health and diagnosing respiratory lesions. The main drawback of mainstream spirometers is that they require the patient to breath through a tube so that the volume and/or flow of the patient's breath can be measured. Breathing through the device introduces resistance to the flow of breath, changing the breathing pattern of the patient. Therefore, it is not possible to use these devices to accurately measure the patient's normal breathing. Breathing through the device requires a conscious, compliant patient. Also, in order to record the metrics suggested by ATS and ERS, patients must undergo a burdensome breathing maneuver, which is such that most elderly, newborn, and neonates can undergo such tests. Excludes patients and COPD patients. Also, treatment outcomes are highly variable depending on patient effort and coaching, as well as operator skill and experience. The ATS also recommends extensive training for healthcare professionals practicing spirometry. Also, many physicians do not have the skills needed to correctly interpret the data obtained from lung function tests. According to the American Thoracic Society, the biggest source of within-subject variability is improper testing. Therefore, most intra- and inter-patient variability in lung function tests is created by human error. Impedance-based respiration monitoring fills an important void because current spirometry cannot provide continuous measurements due to patient cooperation and requirements for breathing through the tube. .. Therefore, it provides near real-time information over long periods (relative to spirometry tests lasting less than 1 minute) in non-intubated patients who may show respiratory changes associated with provocation tests or interventions. There is a need for devices.

To obtain acceptable spirometry as specified by the ATS standard, healthcare professionals must undergo extensive training and refresher courses. One group showed that the amount of acceptable spirometry was significantly higher for those who underwent the training workshop (41% vs. 17%). Even with acceptable spirometry, the interpretation of the data by the attending physician was considered incorrect by the pulmonologist with a 50% probability. However, it was noted that assistance from computer algorithms showed an improvement in interpreting spirograms when sufficient spirometry was collected.

Strict training is required for primary care clinics to obtain acceptable spirometry and make accurate interpretations. However, the resources to train large numbers of people and enhance satisfactory quality assurance are irrational and inefficient. Even in dedicated research settings, technician performance declines over time.

In addition to human error due to patients and healthcare providers, spirometry includes systematic errors that compromise respiratory variability measurements. Useful measurements of breath-to-breath patterns and variability have been shown to be exacerbated by airway fittings such as face masks or mouthpieces. Also, the discomfort and inconvenience associated with measurements with these devices prevents them from being used for routine measurements or as long-term monitors. Other less invasive techniques such as thermistors or strain gauges have been used to predict changes in volume, but these methods provide poor information about respiratory volume. Breathing belts have also been shown to be promising for measuring respiratory volume, but the group found that they were less accurate and had greater variability than measurements from impedance pneumographies. I have shown. Therefore, there is a need for a system that can measure volume over a long period of time with minimal patient and clinician interaction.

Pulmonary function tests and pre- and post-operative care Pre-operative care identifies what patient characteristics may put the patient at risk during surgery and minimizes those risks. Concentrate on. Medical history, smoking history, age, and other parameters specify the steps taken in preoperative care. Specifically, elderly patients and patients with lung disease can be at risk for respiratory complications when placed under ventilator for surgery. To clear these patients with regard to surgery, lung function tests such as spirometry are performed, which gives more information to determine if the patient has access to a ventilator. .. Also, chest x-rays can be taken. However, these tests cannot be repeated in the middle of surgery or in anesthetized patients, or in patients who are unable or will not be coordinated. The examination may be uncomfortable in the post-operative setting and may adversely affect patient recovery.

End tidal CO ₂ and patient monitoring end tidal CO ₂ is another useful metric for determining the state of the patient's lungs. The value is presented as a percentage or partial pressure and is measured continuously using a capnograph monitor, which can be linked with other patient monitoring devices. These instruments, creating a capnogram, which represents the concentration of CO ₂ waveform. Capnography compares the carbon dioxide concentration in exhaled air with the carbon dioxide concentration in arterial blood. The capnogram is then analyzed to diagnose respiratory problems such as hyperventilation and hypoventilation. End-tidal CO ₂ trends are particularly useful for assessing ventilator performance and for identifying drug activity, technical problems associated with intubation, and airway obstructions. The American Society of Anesthesia (ASA) recommends that end-tidal CO ₂ be monitored whenever an endotracheal tube or laryngeal mask is used, and also for any treatment that requires general anesthesia. Ordered to be highly encouraged. Capnography was also found to be more useful than pulse oximetry for monitoring patient ventilation. Unfortunately, it is generally inaccurate and difficult to achieve in non-ventilated patients, and other complementary respiratory monitoring methods will have great utility.

Echocardiography Fenichel et al. determined that respiratory movements can cause interference with echocardiography if it is uncontrolled. Respiratory motion can block anterior echoes through the expansion of the lungs, which in turn causes the angle of incidence of the transducer ray on the heart. These effects on the echocardiographic signal may reduce the accuracy of the measurements recorded or inferred from the echocardiogram. Combining echocardiography with accurate measurement of respiratory cycles can allow the imaging device to compensate for respiratory motion.

Impedance Pneumography Impedance Pneumography is a simple method that can provide breath volume tracing without obstructing air flow, does not require contact with an air stream, and does not limit body movement. Moreover, it may be able to make measurements that reflect the functional residual capacity of the lungs.

While attempting to measure cardiac activity, Atzler and Lehmann noted that transthoracic electrical impedance changes with breathing. They considered the change in respiratory impedance as an artifact and asked the patient to stop breathing while the measurements were being made. In 1940, and while studying cardiac impedance, Nyboer noted the same respiratory impedance artifact in his measurements. By using a spirometer to simultaneously record both transthoracic impedance changes and volume changes, he became the first person to relate transthoracic impedance changes to volume changes, thus The cause was confirmed. Goldenson and Zablow utilized impedance pneumography as a further step by becoming the first investigators to quantitatively relate respiratory volume and transthoracic impedance. They reported difficulties in isolating cardiac signal artifacts and also noticed artifacts during movement of the body. However, after comparing changes in impedance and changes in respiratory volume by least-squares regression, they importantly determined that the two were linearly related. Another group confirmed the linear relationship between changes in transthoracic impedance and respiratory volume and found that approximately 90% of the spirometry signal could be explained by the thoracic impedance signal. While the relationship was shown to be linear, many groups found that intra- and inter-patient calibration constants were highly variable between trials. The difference in these calibration constants can be attributed to various physiological and electrode properties, which must be taken into account.

Theory of Transthoracic Impedance Electrical impedance is a complex quantity defined as the sum of resistance (R), real component, and reactance (X), imaginary component (Z=R+jX=|Z|e ^{j Θ} ). It is used as a measure of opposition to alternating current. Mathematically, impedance is given by the following equation, which is similar to Ohm's law:
Z=V/I (1)
Measured by Here, voltage=V, current=I, and impedance=Z. An object that conducts electricity with an unknown impedance can be determined from a simple circuit. Applying a known alternating current across an object while measuring the voltage across the object and using equation (1) results in impedance. The rib cage represents a volume conductor and hence the rules governing the ionic conductor may be applied. In addition, movement of organs and expansion of the rib cage during breathing produces a change in conductivity, which can be measured. Impedance across the thorax can be measured by introducing a known current and by measuring changes in voltage across the thorax with electrodes.

Origin of Transthoracic Impedance Signal All of the tissue layers that make up the thorax and abdomen affect the measurement of transthoracic impedance. Each tissue has a different conductivity that affects the direction of current flow between the electrodes. Starting from the outermost layer, the surface of the body is covered by the skin, which exhibits a high resistivity, but only about 1 mm thick. Below the skin is a layer of fat, which also has a high resistivity. However, the thickness of this layer is highly variable and depends on the location and body type of the subject's body. Going from posterior to anterior, there are postural muscles below the skin and fat layers, which are anisotropic. They have a low resistivity in the longitudinal direction but a high resistivity in all other directions, which leads to a tendency to conduct current in a direction parallel to the skin. Below the muscles are the ribs, which are, as bones, very insulating. Thus, current through the rib cage can only flow between bones. When the current reaches the lungs, it is assumed that the current travels through the blood, which has one of the lowest resistances of any body tissue. Lung ventilation changes the size of the lungs and the path of current flow, manifesting itself as a change in resistance or impedance, which can be measured.

Due to the anisotropic nature of the tissue, the radial current flow through the chest is much less than would be expected. Most of the electric current travels around the chest, rather than through the chest. As a result, changes in impedance result from changes in the outer circumference of the rib cage, changes in lung size, and movement of the diaphragm-liver mass. At lower rib cage levels, measurements are attributed to diaphragm and liver migration, and at higher rib cage levels, they are attributed to lung ventilation and inflation. Thus, the impedance signal is the sum of changes in lung inflation and insufflation, and diaphragm-liver mass migration. Both the abdominal and thoracic components are needed to observe normal respiratory signals. In addition, the different origins of impedance changes in the upper and lower rib cages may explain why greater linearity is observed at higher rib cage levels.

Effect of Electrode Placement Transthoracic impedance is measured by electrodes attached to the patient's skin. Geddes et al. determined that the electrode stimulation frequency should not be below 20 kHz due to physiological tissue considerations. It is a matter of safety and elimination of interference from bioelectric phenomena. In addition, it has been discovered that subject impedance measurements are dependent on subject position, including sitting, supine and standing. For a given change in volume, supine position has been shown to result in maximum signal amplitude and minimal signal-to-noise during breathing.

Another potential signal artifact comes from the subject's movement, which may move the electrodes and disrupt the calibration. Moreover, electrode migration may occur more frequently in obese and elderly patients, which may require repeated lead recalibration during periods of long-term monitoring. It has been suggested, in part, that calibration should be performed for each individual for a given subject pose and electrode placement because of calibration variability between trials. However, one group could show that careful electrode placement within the patient can reduce the impedance difference between measurements to approximately 1%.

Despite having the same electrode placement, calibration constants and signal amplitudes for individuals of different sizes showed variability. It was determined that the change in impedance for a given change in volume was greatest for people with thin chests and smaller for people of fuller size. These observed differences indicate that there is a greater amount of resistant tissue, such as adipose tissue and muscle, between the electrodes and lungs in larger subjects, and for a given subject a given change in volume. May be attributed to causing a smaller percentage change in impedance as a whole. On the other hand, in children it can be noticed that the cardiac component of the impedance trace is larger than in adults. This may be due to the greater deposition of fat around the heart in adults than in children, which serves to shield the heart from being incorporated into impedance measurements.

Electrodes attached to the mid-axillary line at the level of the 6th rib resulted in maximum impedance changes during breathing. However, maximum linearity between the two variables was achieved by placing the electrodes higher above the thorax. Despite the high degree of linearity reported, large standard deviations of impedance changes during breathing have been reported. However, the variability observed in impedance measurements is comparable to that seen in other vital sign measurements, such as blood pressure. Several groups have shown that impedance pneumography methods are sufficiently accurate for clinical purposes. Moreover, in the 40 years since these studies, electrode materials and signal processing for impedance measurement have been greatly improved, leading to even more reliable measurements. Digital signal processing allows near-instantaneous filtering and smoothing of real-time impedance measurements, which allows the minimization of artifacts and noise. More recently, respiratory impedance has been used successfully in long-term patient monitoring. As long as the electrodes remain relatively immovable, the relationship of impedance change to volume change remains stable over time.

Active Acoustic Systems The most common use of acoustics associated with the lungs is to assess the sound produced in the lungs acquired by the use of a stethoscope. One property of lung tissue that is often overlooked is its ability to act as an acoustic filter. It attenuates different frequencies of the sound passing through it to different extents. There is a relationship between the level of damping and the amount of air in the lungs. Also, chest wall movement results in a frequency shift of the acoustic signal passing through the rib cage.

Possibility of Detecting Abnormalities Many useful indicators, such as forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV ₁ ), were extracted from monitoring the volume traces of the patient's breath by impedance pneumography. obtain. FVC and FEV1 are two benchmark indicators that are typically measured by spirometers and are used to diagnose and monitor diseases such as COPD, asthma and emphysema. In addition to monitoring respiration, impedance pneumography is also capable of simultaneously recording electrocardiograms from the same electrode.

Breath-to-breath variability Calculations such as breath-to-breath variability, coefficient of variance, standard deviation, and tidal volume histogram symmetry have been shown to be age and respiratory health dependent. It has been shown that some of these parameters, in particular the coefficient of variance, differ significantly in patients suffering from tuberculosis, pneumonia, emphysema, and asthma compared to normal subjects. Moreover, it is noted in the literature that impedance measurements were satisfactory as long as the electrodes did not move over the patient. In general, it has been determined by many groups that healthy subjects exhibit greater breathing pattern variability than subjects with lung disease.

Non-linear analysis of respiratory waveforms is used in a variety of applications. In a study of the regularity of non-linear physiological data, studies have shown that patients exhibit reduced breath-to-breath complexity in lung disease states. This reduction in complexity has been demonstrated in patients with chronic obstructive pulmonary disease, restrictive pulmonary disease, and failure to extubate from mechanical ventilation. It has also been determined that the reduced variability is a result of sedation and analgesia. On a large scale, normal patients have greater breath-to-breath variability than patients suffering from some form of lung disease or disorder.

Respiratory patterns, like any physiological data, are non-linear because they are affected by many regulatory factors in the body. In the analysis of breath-to-breath variability, various entropy metrics are used to measure the amount of irregularity and reproducibility in the signal. These metrics include RVM tidal volume tracings in assessing not only breath-to-breath variation, but also variability in continuous breathing, as well as curve size, periodicity, and spatial location. Can be used in the analysis of.

A universal calibration of the system based on standardized patient characteristic data (Crapo) allows the generation of a complexity index, and also defines what is defined as the normal level of complexity and a single patient. Allows comparison. This index will be used to assist clinicians in determining the appropriate time for extubation, in determining the severity of cardiopulmonary disease, and in assessing treatment regimens. Become. This index will be independent of how the data is collected, such as through an impedance-based device, accelerometer, ventilator, or imaging device. The system can also be calibrated for a particular patient to focus on intra-subject variability while detecting sudden changes in any of the respiratory parameters.

Non-Linear Analysis of Interbreath Intervals In addition to variability metrics, some groups have found that non-linear analysis of instantaneous interbreath intervals is highly correlated to the success of weaning from mechanical ventilators. discovered. These metrics are useful indicators of lung health and can support clinical decisions. The inability of patients to separate from mechanical ventilators occurs in approximately 20% of patients, and current methods for predicting successful separation are poor and rarely aid physician decisions. In a study of 33 subjects who had been under mechanical ventilation for more than 24 hours, 24 subjects were successfully weaned from ventilation, while 8 subjects failed (1 Data from one subject was removed). Reasons for failure included hypoxia in 5 subjects and tachypnea, hypercapnia, and upper airway edema in the remaining 3 subjects, all of which were potentially impedance-neutral. A disease that can be identified by a morphography system. The main finding in this study was that the non-linear analysis of instantaneous breath intervals for those who failed to separate from mechanical ventilators was significantly more regular than those who successfully separated. Moreover, it was shown that respiratory rates did not differ between the two groups. Metrics derived from non-linear analysis of impedance pneumograph measurements can successfully predict patient outcomes. In addition, these metrics have been shown to be robust and do not change significantly when artifacts such as coughing are introduced.

Decreased Ventilation Detection Respiratory traces produced by impedance pneumography and the subject's average impedance can indicate reduced ventilation or changes in fluid volume in the thorax. This type of monitoring will be useful in the care of anesthetized patients. Respiratory monitoring by impedance pneumography in anesthetized or immobile patients has been shown to be accurate and reliable over long periods of time, especially during critical periods in recovery rooms following surgery. Fluids in the rib cage or lungs can lead to measurable changes in impedance, which are used to determine common problems for patients in the recovery room, such as lung edema or pneumonia. The investigator decided to get it.

In addition to measuring changes in fluid volume in the thorax, changes in tidal volume and upper airway resistance are immediately apparent in impedance measurements. The investigators have found that endotracheal clamping of a patient during anesthesia still produces a small impedance signal regardless of the patient's effort to breathe, thereby providing the correct indication of ventilation. Impedance measurements have also been shown to provide a quantitative assessment of ventilation in each lung. In patients with only one lung lesion, a pair of electrodes on the injured side of the rib cage produced a weaker signal than on the normal side, and differences in impedance measurements were observed.

Respiratory Monitor So far, certain contact probes record respiratory rates while recording or analyzing respiratory patterns or variability, correlating respiratory patterns or variability with physiological conditions or viability, or No device or method has been specifically devised to predict impending collapse using breathing patterns or variability. Heart rate variability algorithms only report heart rate to heart rate variability. It is desirable to use a respiratory rate variability algorithm to incorporate respiratory intensity, respiratory rate, and location variability of respiratory movements. Significant respiratory abnormalities, as noted by changes in intensity, changes in respiratory rate, changes in the localization of respiratory effort, or changes in the variability of any of these parameters, can cause respiratory or cardiovascular failure. It is possible to provide early warning and present opportunities for early intervention. The development of devices to record these changes and the generation of algorithms that correlate these respiratory changes with the severity of the disease or injury is not only a useful battlefield tool, but also to evaluate and treat critically ill patients. To help one of the importance in the setting of hospital emergency care. Use in a clinic or home setting can help non-critically ill patients, who may nevertheless benefit from such monitoring. For example, if the patient is over-anesthetized, the respiratory rate will drop and the breath will be "shallow". Respiratory rate and effort are increased by hard lung and poor air exchange due to pulmonary edema or other reasons for loss of lung compliance. However, the only objectively monitored parameter, the implication of velocity, is often not identified fast enough to best treat the patient. Can provide a real-time quantitative assessment of work of breathing and can analyze trends in respiratory rate, intensity, localization, or variability of any or all of these parameters Systems are needed for early diagnosis and intervention, as well as treatment monitoring. Such systems are needed to determine the depth of anesthesia or the adequacy or overdose of an anesthetic or other analgesic drug.

PCA and Feedback Control Patient-controlled analgesia (PCA) is a method of post-operative pain control that involves patient feedback. Administration of opiates may suppress respiration, heart rate, and blood pressure, thus requiring careful and close monitoring. The system includes a computerized pump, which contains an analgesic, which can be pumped into the patient's IV line. Generally, in addition to a fixed dose of an analgesic, the patient can press a button to receive additional drug form care. However, the patient is discouraged from pushing the button if he is excessively sleepy, as this may interfere with therapy for a faster recovery. Safeguards are also in place to limit the amount of drug given to a patient at a given amount of time to prevent overdose. A pulse oximeter, respiration rate, and capnographic monitor can be used to warn of respiratory depression caused by analgesics and cut off PCA dosing, but each is at least accurate, effective, And has serious limitations regarding implementation.

Respiratory Assist Devices Chronic obstructive pulmonary disease (“COPD”), emphysema, and other illnesses have the effect of reducing the ability of patients to provide efficient exchange of air and provide proper breathing. There is. COPD is a disease of the lungs that makes it difficult to breathe. It is usually caused by years of lung damage from smoking. COPD is often a mixture of two diseases: chronic bronchitis and emphysema. In chronic bronchitis, the airways that carry air to the lungs become inflamed and produce large amounts of mucus. This can narrow or block the airways, making it difficult to breathe. In a healthy person, the small air sacs in the lungs look like balloons. As a person breathes in and out, the air sacs grow and shrink as they move air through the lungs. However, in the case of emphysema, these air sacs are damaged and have lost their elasticity. Less air enters and leaves the lungs, which causes lack of breathing. COPD patients often have difficulty obtaining adequate oxygenation and/or CO2 removal, and their breathing can be difficult and labor intensive.

Cystic fibrosis (“CF”), also known as mucobicidosis, is a genetic disease that primarily affects the lungs as well as the pancreas, liver, kidneys, and intestine. Long-term problems include difficulty breathing and coughing and spitting sputum as a result of frequent lung infections. Other symptoms include, among others, sinus infections, poor growth, steatorrhea, the toes of the fingers and toes, and male infertility.

There are numerous therapies used to help alleviate the symptoms of COPD, CF, emphysema, and other respiratory problems. For example, a patient can wear a High-Frequency Chest Wall Oscillation (“HFCWO”) vest or oscillator. The HFCWO vest is an inflatable vest attached to a machine that vibrates it at high frequencies. The vest vibrates the chest to loosen and thin the mucus. Alternatively, the patient uses a continuous positive airway pressure (“CPAP”) or bilevel positive airway pressure (“BiPAP”) device on a continuous basis. It is possible to maintain an open airway in a patient who provides air pressure and is able to breathe on his own. Other mechanical ventilation therapies include cuff assist systems, oxygen therapy, aspiration therapy, CHFO (“Continuous High Frequency Oscillation”) (Continuous High Frequency Oscillation), ventilators, medicated aerosol delivery systems, and other non-invasive systems. Ventilation methods, including but not limited to.

Each of these therapy methods has common drawbacks, and there is no way to know how much air is actually in the lungs. Some therapies use pneumatic pressure feedback to time-efficient oxygen therapy. This may be inaccurate and is not a direct measure of oxygen ventilation. Moreover, therapy using masks may be inaccurate due to leaks and problems associated with mask placement. Additionally, kinking and dysfunction in the pneumatic airway circuit can provide inaccurate measurements of the amount of air entering the lungs.

The present invention overcomes the problems and disadvantages associated with current strategies and designs and provides new systems and methods for patient monitoring.

A preferred embodiment of the present invention is directed to a non-invasive ventilation therapy system. The system includes a ventilation device, a computing device coupled to the ventilation device, and a plurality of sensors operatively connected to the computing device for obtaining a physiological bioelectrical impedance signal from a patient. The computing device receives the physiological bioelectrical impedance signal from the sensor, analyzes the physiological bioelectrical impedance signal, and sends the signal to the ventilation device based on the analyzed physiological bioelectrical impedance signal, Adjust therapy level.

In a preferred embodiment, the computing device further provides an assessment of the patient's minute ventilation, tidal volume, and respiratory rate based on the analyzed bioelectrical impedance signal. Preferably, the therapy level is at least one of frequency, intensity, pressure, and length of therapy. The system preferably further comprises an aerosol delivery system.

The computing device preferably further monitors lung performance from session to session to determine efficacy of therapy. Preferably, the non-invasive ventilation device is a high frequency chest wall vibration (“HFCWO”) vest, a continuous high frequency vibration (“CHFO”) system, a ventilator, a continuous positive pressure breathing therapy (“CPAP”) device, a bypass device. With one of a Level Positive Airway Pressure (“BiPAP”) Device, Continuous Positive Expiratory Pressure (“CPEP”) Device, Another Mechanical Ventilation Device, Oxygenation Therapy Device, Aspiration Therapy Device, and Cuff Assist Device is there. In a preferred embodiment, the computing device further outputs a bioimpedance exhalation/suction curve and determines the efficacy of therapy based on the bioimpedance exhalation/suction curve.

Preferably, a plurality of sensors are placed on the patient's torso and the physiological bioelectrical impedance signal is measured transthoracically. The system preferably further comprises a pulse oximeter to measure patient oxygenation. Preferably, the ventilation device causes mobilization of fluid in the lungs.

Another embodiment of the invention is directed to a method of providing a non-invasive ventilation therapy system. A method includes providing a ventilation device to a patient, coupling a plurality of sensors to a patient to obtain a physiological bioelectrical impedance signal, and coupling the ventilation device and the plurality of sensors to a computing device. Including and The computing device receives the physiological bioelectrical impedance signal from the sensor, analyzes the physiological bioelectrical impedance signal, and adjusts the therapy level of the ventilation device based on the analyzed physiological bioelectrical impedance signal. ..

Preferably, the computing device further provides an assessment of the patient's minute ventilation, tidal volume, and respiratory rate based on the analyzed bioelectrical impedance signal. In a preferred embodiment, the therapy level is at least one of frequency, intensity, pressure, and length of therapy. Preferably, the method further comprises coupling the aerosol delivery system to the patient and the computing device.

The computing device preferably further monitors lung performance from session to session to determine efficacy of therapy. Preferably, the non-invasive ventilation device is a high frequency chest wall vibration (“HFCWO”) vest, a continuous high frequency vibration (“CHFO”) system, a ventilator, a continuous positive pressure breathing therapy (“CPAP”) device, a bypass device. With one of a Level Positive Airway Pressure (“BiPAP”) Device, Continuous Positive Expiratory Pressure (“CPEP”) Device, Another Mechanical Ventilation Device, Oxygenation Therapy Device, Aspiration Therapy Device, and Cuff Assist Device is there. The computing device preferably further outputs a bioimpedance exhalation/suction curve and determines the effectiveness of the therapy based on the bioimpedance exhalation/suction curve.

In a preferred embodiment, multiple sensors are placed on the patient's torso and physiological bioelectrical impedance signals are measured transthoracically. The method preferably further comprises the step of coupling a pulse oximeter to the patient and the computing device for measuring patient oxygenation. Preferably, the ventilation device causes mobilization of fluid in the lungs.

Other embodiments and advantages of the invention are set forth, in part, in the description that follows, may in part be apparent from this description, or may be learned from practice of the invention.

The present invention is described in greater detail by way of example only and with reference to the accompanying drawings.

FIG. 6 is a perspective view of a four lead wire embodiment of the present invention. It is a diagram of a rear left and right electrode configuration. It is a diagram of a rear right vertical electrode configuration. 3 is a diagram of an electrode configuration from the front side to the back side. It is a diagram of a front right vertical electrode configuration. FIG. 6 is a perspective view of a two 4-lead configuration connected together by a multiplexer. 3 is a diagram of an ICG electrode configuration. FIG. 5 is a perspective view of a four lead embodiment of the present invention connected to a spirometer. FIG. 6 is a perspective view of a four lead embodiment of the present invention connected to a ventilator. 3 is a plot of RVM measurement (impedance) versus volume for slow breathing, normal breathing, and unstable breathing. FIG. 6 shows a set of plots of RVM and volume against time for normal breathing. FIG. 6 shows a set of plots of RVM and volume against time for slow breathing. FIG. 6 shows a set of plots of RVM and volume against time for unstable breathing. 4 is a plot of calibration factor against BMI for four different electrode configurations. 9 is a spirometry plot showing volume drift. 3 is a plot of volume vs. impedance as affected by volume drift. 9 is a plot of spirometry corrected for volume drift. 3 is a plot of volume versus impedance comparing uncorrected and corrected data for volume drift. It is a flowchart explaining the data analysis regarding this invention. 3 is a preferred embodiment of the present invention utilizing a speaker and a microphone. 3 is a preferred embodiment of the present invention utilizing an array of speakers and microphones. 3 is a preferred embodiment of the present invention utilizing an array of speakers and a microphone. 3 is a preferred embodiment of the present invention utilizing a vest for a sensor. 3 is a preferred embodiment of the present invention utilizing an array incorporated into a cloth strip for a sensor. 3 is a preferred embodiment of the present invention utilizing a sensor net. 3 is a preferred embodiment of the present invention utilizing a wireless transmitter and receiver. 3 is a graph of impedance vs. time and volume vs. time for simultaneously recorded data. FIG. 3 illustrates an embodiment of the system of the present invention. FIG. 6 illustrates an embodiment of the device of the present invention. FIG. 5 illustrates a preferred embodiment of the device of the present invention. FIG. 5 illustrates a preferred embodiment of the device of the present invention. FIG. 5 illustrates a preferred embodiment of the device of the present invention. It is a figure which shows different embodiment of installation of a lead wire. It is a figure which shows different embodiment of installation of a lead wire. It is a figure which shows different embodiment of installation of a lead wire. It is a figure which shows different embodiment of installation of a lead wire. It is a figure which shows different embodiment of installation of a lead wire. It is a figure which shows different embodiment of installation of a lead wire. FIG. 6 illustrates an embodiment of a modified Howland circuit for compensating for parasitic capacitance. FIG. 6 illustrates an embodiment of the present invention in which an impedance measuring device is in data communication with a HFCWO vest. FIG. 6 illustrates an embodiment of the present invention in which an impedance measurement device is in data communication with a mechanical ventilation therapy device. FIG. 6 illustrates an embodiment of the invention in which an impedance measurement device is in data communication with an oxygenation therapy device. FIG. 6 illustrates an embodiment of the invention in which an impedance measurement device is in data communication with an aspiration therapy device. FIG. 6 illustrates an embodiment of the present invention in which an impedance measurement device is in data communication with a cuff assist device.

This disclosure, as embodied and broadly described herein, now provides the detailed embodiments of the invention. However, the disclosed embodiments are merely exemplary of the invention, which may be embodied in various and alternative forms. Therefore, there is no intention that the specific structural and functional details should be limiting, but rather that they provide the basis for the claims and the invention. Is provided as a representative basis for teaching those skilled in the art of various uses.

One embodiment of the invention is directed to a device for assessing a patient, individual, or animal, which provides impedance measurement by placing multiple electrode leads and/or speakers and microphones on the body. Collect values. Preferably, the at least one impedance measuring element and the microphone/speaker are operatively connected to a programmable element, the programmable element being programmed to provide an assessment of at least one respiratory parameter of the subject.

Preferably, the impedance measurement is based on a plurality of remote probe datasets and the programmable element is adapted to enhance at least one of the plurality of remote probe datasets; or of the plurality of remote probe datasets. Of the plurality of remote probe data sets for dynamic range and signal-to-noise ratio (SNR) values. Preferably, the device probe is maintained in several lead configurations. In one embodiment, the modification of the lead configuration allows flexibility depending on the subject and the test being performed. In another embodiment, the modification of the lead configuration allows for variability in the patient's anatomy. Preferably, the device maintains settings to identify valid lead configurations. Preferably, the device maintains settings to identify valid lead attachments.

Preferably, a device or method as described in the protocol embedded in the machine will direct the placement of the leads. Preferably, proper lead contact is verified by the device. Preferably, the device alerts the operator regarding poor or improper lead setting.

Preferably, the device monitors continuously or intermittently and maintains an alarm to indicate when respiratory parameters reflect ventilation or other loss of biological function. Alarms are variability in respiratory satisfaction index, minute ventilation, respiratory rate, tidal volume, inspiratory volume or flow parameter, expiratory volume or flow parameter, respiratory rate, volume, flow, or other parameter generated. It is set based on. For example, if the monitor detects a decrease in either respiratory frequency or depth or minute ventilation associated with hypoventilation, or any of these parameters that would indicate hyperventilation, or If any increase is detected, an alarm will be activated. Alarms are used on the hospital floor in comparing the patient's current respiratory status to a baseline level based on a particular individual's calibration for a ventilator or spirometer. Preferably, the alarm is set based on parameters taken from a given individual from the ventilator or spirometer. More preferably, the baseline level is based on one or more of the following: demographic, physiological, and somatic parameters. Alarms are also used to alert anesthetic-induced respiratory depression at points that are determined to be harmful to the patient. Preferably, the range of values over which the alarm will be triggered is determined by the doctor or caregiver as follows: respiratory rate, tidal volume, minute ventilation, respiratory satisfaction index, respiratory curve. Shape, entropy, fractal, or one or more of other analytical parameters associated with respiratory variability or complexity.

In another embodiment, the RVM measurements taken at any given time are recorded as the baseline. These recorded values are correlated to the subjective impression of the patient's condition by the physician or other health care worker. Thereafter, the RVM is monitored and if there is a 10%, 20%, or other selected percentage change in respiratory volume, minute ventilation curve characteristics or variability, an alarm is issued by the healthcare staff. Is set to alert.

The following illustrates embodiments of the invention, but should not be seen as limiting the scope of the invention.

Impedance Plethysmographs As embodied and broadly described herein, there are provided detailed embodiments of the present invention. The embodiments are merely exemplary of the present invention, which may be embodied in various and alternative forms. Therefore, there is no intention that the specific structural and functional details should be limiting, but rather that they provide the basis for the claims and the invention. Is provided as a representative basis for teaching those skilled in the art of various uses.

The present invention preferably converts the measured impedance value to a volume and uses a numerical or graphical representation of the data to display the volume to an end user through an electronic interface or printed report. , Impedance Pneumograph with integrated electronics. The impedance measuring device includes a circuit, at least one microprocessor, and preferably at least four leads. Preferably, at least two leads are used to inject current into the body of the subject and at least two are used to read the voltage response of the patient's body.

In one embodiment, the device preferably includes integrated modules to simulate a patient and enable automated system testing and demonstration. Automated system testing improves device performance and ensures that it is working properly before use.

In the preferred embodiment, the device utilizes analog dividers to compensate for small deviations in the injected current and to improve the accuracy of the acquired data. In the preferred embodiment, the analog divider will be installed after the demodulator and before the rectifier. In other embodiments, the analog divider may be installed elsewhere in the circuit, including without limitation, after the precision rectifier or before the demodulator.

In a preferred embodiment, the device is an adaptive electronic driven by a microprocessor to maintain the proper gain above the different amplifiers in the circuit to prevent the signal from going out of range. Use equipment. The microprocessor tracks the gain set in each of the hardware amplifiers and compensates appropriately during its calculation so that it always outputs the appropriate value.

The impedance measurement device is preferably connected to the computer via a digital interface (eg, USB, Firewire, serial, parallel, or other type of digital interface). Digital interfaces are used to prevent data corruption during transfer. Communication via this interface is preferably encrypted to further guarantee the integrity of the data and to protect the invention from the use of counterfeit modules (either measuring devices or computers).

Referring now in more detail to the preferred embodiment of the present invention, FIG. 1 shows an impedance plethysmograph, which is housed in a radio frequency impedance meter 1 and a PC linked to the meter. The radio frequency impedance meter 1 including a programmable element 2 including four lead wires, that is, a first lead wire 3, a second lead wire 4, a third lead wire 5, and a fourth lead wire 6. Connected to the patient by. Each lead wire is preferably connected to a surface electrode, ie a first surface electrode, a second surface electrode, a third surface electrode and a fourth surface electrode.

More specifically, referring still to the embodiment of FIG. 1, the electrodes may be made from a conductive material such as AgCl coated with an adhesive conductive material such as a hydrogel or hydrocolloid. The leads may be made of any electrically conductive material such as copper wire or the like, preferably coated with an insulating material such as rubber or the like. In the preferred embodiment, wireless electrodes are utilized to provide electrical current, as well as to collect and transmit data. Preferably, this lead configuration is coupled with Bluetooth technology and the receiver.

Leads 1 and 4 are connected to a current source at a constant frequency large enough to avoid interfering with biological signaling, preferably greater than 20 KHz. The amplitude of the current source is preferably less than 50 mA and is below the level that will cause fibrillation at the selected frequency. The differential voltage between leads 2 and 3 is used to calculate the impedance according to Ohm's law. By sampling the voltage measurements taken by the impedance meter, the programmable element (eg, PC, etc.) tracks and plots changes in thoracic impedance that correspond to biological functions such as heartbeat and respiration. The change in impedance is then used to monitor lung function. Preferably, the device is calibrated by the methods described herein to calculate the lung volume and display it to the operator.

Referring to FIG. 28, an exemplary and suitable system includes at least one general purpose computing device 100, which includes a processing unit (CPU) 120 and a system bus 110. The system bus 110 couples various system components, including system memory, such as read only memory (ROM) 140 and random access memory (RAM) 150, to the processing unit 120. Other system memory 130 may also be available for similar use. The present invention preferably operates on a computing device with more than one CPU 120, or on a group or cluster of networked computing devices to provide higher processing power. Works with. System bus 110 can be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. is there. A basic input/output (BIOS), such as stored in ROM 140, preferably provides the basic routines, which provide information between elements within computing device 100, such as during start-up. Help transfer. Preferably, computing device 100 further comprises a storage device such as a hard disk drive 160, magnetic disk drive, optical disk drive, tape drive, or the like. The storage device 160 is connected to the system bus 110 by a drive interface. The drives and their associated computer-readable media provide nonvolatile storage of computer-readable instructions, data structures, program modules, and other data for computing device 100. These basic components are known to the person skilled in the art, suitable variants being, depending on the type of device, for example a handheld computing device with a small device, a desktop computer, a laptop computer, a computer server, It is contemplated depending on whether it is a wireless device, a web-enabled device, a wireless phone, or the like.

In some embodiments, the system is preferably controlled by a single CPU, but in other embodiments, one or more components of the system are controlled by one or more microprocessors (MPs). Controlled. Additionally, a combination of CPU and MP may also be used. Preferably, the MP is a built-in microcontroller, but other devices capable of processing commands can also be used.

Although the exemplary environment described herein uses hard disks, magnetic cassettes, flash memory cards, digital versatile disks, cartridges, random access memory (RAM), read only memory (ROM), and , Other types of computer-readable media capable of storing computer-accessible data, such as cable signals containing bitstreams, wireless signals, or the like, may also be used in the exemplary operating environment. Should be recognized by. To enable user interaction with the computing device 100, the input device 190 may be, for example, a microphone for speech, a touch-sensitive screen for gesture or graphical input, an electrical signal sensor, a keyboard, a mouse, motion input. , And speech represent any number of input mechanisms. The device output 170 can be one or more of a number of output mechanisms known to those skilled in the art, such as printers, monitors, projectors, speakers, and plotters, for example. In some embodiments, the output may be, for example, uploaded to a website, emailed, attached or placed in another electronic file, and sent an SMS or MMS message to the network. It is possible via an interface. In some cases, a multi-modal system allows a user to provide multiple types of inputs for communicating with computing device 100. Communication interface 180 generally governs and manages user input and system output. There is no limitation on the invention that operates on any particular hardware construct, and the basic features herein are therefore readily apparent to those as the improved hardware or firmware constructs are developed. Can be replaced.

Embodiments within the scope of the present invention may also include computer-readable media for carrying or having computer-executable instructions, or data structures stored on the computer-readable media. Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer readable media include RAM, ROM, EEPROM, CD-ROM, or other optical disk storage, magnetic disk storage, or other magnetic storage device, or computer. , And any other medium that can be used to carry or store the desired program code means in the form of instructions or data structures. When information is transferred or provided to a computer over a network or another communication connection (whether hard-wired, wireless, or a combination thereof), the computer properly views the connection as a computer-readable medium. .. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of computer-readable media.

Computer-executable instructions include, for example, instructions and data that cause a general purpose computer, special purpose computer, or special purpose processing device to perform a particular function or group of functions. Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represent examples of corresponding acts for implementing the functions described in such steps.

Other embodiments of the invention include many types of computers, including personal computers, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, and mainframe computers. Those skilled in the art will understand that they can be practiced in a network computing environment with a computer system configuration. The network may be the Internet, one or more local area networks (“LAN”), one or more metropolitan area networks (“MAN”), one or more wide area networks (“WAN”), one or more intranets, etc. Can be included. Embodiments may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices, where local and remote processing devices (via hardwired links, Wireless links, either by wireless links or a combination thereof). In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

FIG. 2 is a schematic diagram of an embodiment of the system 200 of the present invention. An electrical source originates from the signal source 205. Preferably, an adjustable function generator 210 (eg, XR2206 chip) is used to generate the electrical source. The function generator 210 is preferably adjustable via a microprocessor (MP) 275 or manually. In some embodiments, the function generator may be tuned to improve the signal. Tuning can occur once or multiple times. Bioimpedance spectroscopy can be used to detect the level of water hydration at different frequencies, which can be used to calibrate the function generator 210. Similarly, the body fat percentage can be calculated. The signal source 205 also includes a current generator 215 (eg, a Howland circuit). The current generator 215 preferably keeps the source current constant despite changes in pad contact (unless the contact is completely broken). In the preferred embodiment, current generator 215 may be tuned to improve performance, which may be done manually or automatically by MP275. The impedance measurement subsystem can utilize current generating components at one or more frequencies, which can be active at the same time or sequentially. The voltage measurement component can be functionally connected to one or more electrodes. The impedance measurement subsystem can utilize non-sinusoidal currents, such as narrow current pulses. The system can integrate additional sensors such as accelerometers, moisture and acoustic sensors, capnography or oximetry sensors and the like.

In a preferred embodiment, the quality of the pad contact is monitored and an alert is generated when the pad contact is broken or of poor quality that the electronics are too poor to compensate. The signal source 205 can also include a current monitor 220 to calculate the impedance. In the preferred embodiment, the signal source 205 also includes a patient simulator 225. The patient simulator 225 can simulate changes in impedance with the same parameters as an actual patient. The patient simulator 225 may be used to test the system 200 and for circuit calibration.

The signal from the signal source 205 passes through the patient 230 and is received by the sensor 235. Preferably, the sensor 230 includes an input amplifier 240. The input amplifier 240 suppresses the effect of poor or variable pad contact on the measurement. The gain of input amplifier 240 is preferably controlled by MP275 to provide the enhanced signal to other modules. The sensor 230 preferably also includes a signal filter 245 to remove interference such as from a power grid. The signal filter 245 can be a standard high pass filter (such as in FIG. 30), a demodulator (such as in FIG. 31), or another signal filter. Synchronous demodulators are often used to detect bioimpedance changes and to remove motion artifacts in the signal.

In the preferred embodiment, the signal is split into two paths (as in FIG. 32). The first path uses the generator signal as a carrier to demodulate the measured signal. The second path uses a 90 degree phase rotation circuit prior to demodulation. Both demodulated signals can be converted to RMS values using a voltage to RMS converter. Measured separately, the signals are summed and then the square root is calculated. This allows compensation for any phase shift in the subject, as well as separate measurements of resistance and reactance, which are valuable information regarding motion artifact compensation and moisture level, fat percentage, and calibration factor calculation. I will provide a.

Additionally, the sensor 230 can include an analog divider 250, which divides the measured voltage signal by the signal from the current monitoring circuit to calculate the impedance. Sensor 230 also preferably includes a precision rectifier or root mean square-direct current (RMS-to-DC) chip 255 with a low pass filter to remove carrier frequencies. The output of sensor 230 is preferably a DC signal proportional to the patient impedance. The sensor 230 also includes a bandpass filter 260, which can select only the respiration rate by filtering out portions of the signal that do not correspond to respiration. Bandpass filter 260 may be manually calibrated or automatically calibrated by MP275. Preferably, sensor 230 includes a multiplexer 265 controlled by MP275 to accommodate multiple probe pairs. Preferably, there are two probe pairs, but more or less probe pairs are also contemplated. The sensor 230 can also include an output amplifier 270. Output amplifier 270 is preferably controlled by MP 275 and provides a signal to an analog-to-digital converter (ADC) 280 for precision digitization. Oversampling is used to reduce measurement noise, which can come from different sources (eg thermal interference, electronic interference, biological interference, or EM interference). The MP275 commands the ADC to take measurements at the highest cadence possible, and then averages the data acquired over the time interval corresponding to the sampling frequency. The sampling frequency is the frequency of impedance sampling as it is presented to the computer by the impedance measuring device. The frequency is preferably set high enough to monitor all of the fine respiratory features.

The use of controllable gain and oversampling preferably means that the system measures impedance with very high effective accuracy (estimated as 28 bits, or 4 parts pervillion for current implementations). To enable.

Both signal source 205 and sensor 230 are controlled by MP275. The MP275 preferably includes at least one ADC 280 for monitoring signal processing and at least one digital output 285 for controlling digital potentiometers, multiplexers, operational amplifiers, signal generators, and other devices. Preferably MP275 and computer interface (eg, via USB interface, serial interface, or wireless interface).

Preferably, the MP calculates values for respiratory rate (RR), tidal volume (TV), and minute ventilation (MV) and tracks trends in the calculated RR, TV, or MV values, Perform statistical, factorial, or fractal analysis on trends in real time. The MP is able to track the instantaneous and cumulative deviations from the appropriate predicted values for RR, TV, or MV, and calculates the respiratory satisfaction index (RSI).

In a preferred embodiment, the device is: temperature, blood pressure, heart rate, SpO2, EtCO2, arterial blood gas, acceleration/motion, GPS location, height, weight, BMI, OSA diagnostics, CHF, asthma, COPD, ARDS. , OIRD, minute ventilation, cardiac output, end-tidal CO2, oxygen perfusion, ECG, and other electrophysiological measurements of the heart, including but not limited to, and measuring other parameters or disease states. Has the ability to record. In a preferred embodiment, the impedance measuring device simultaneously measures impedance cardiography and impedance pneumography. Preferably, the additional parameters are displayed on the screen. Preferably, respiratory impedance data is combined with additional parameters in a meaningful way to act as a diagnostic aid. Preferably, the impedance data is used alone or in combination with one or more additional parameters to provide a diagnosis of the disease state.

In one embodiment, measurements are taken independently from each side of the chest to determine the overall lung condition and the right lung ventilation or chest inflation and left lung ventilation or chest inflation. Used to evaluate both the differences between. An example of this is the reduction of movement due to splints or pneumothorax in cases of rib fractures, where changes can be attributed to injury, including lung contusions, where both sides of the chest are independent. Monitored to provide specific data for each side. Other causes of localized lung lesions can also be evaluated, including pneumonia, hydrothorax, chylothorax, hemothorax, blood/pneumothorax, atelectasis, tumors, and radiation injury.

In another embodiment, the information from the device is used with information from an echocardiogram, radionuclide test, or other method of imaging the heart. In a preferred embodiment, the device comprises the following: ekg, advanced electrophysiological studies, cardiac catheterization, echocardiography, stress testing, radionuclide testing, CT, MRI, and cardiac output by impedance measurement. One of the monitoring aids in the diagnosis of myocardial ischemia. In one embodiment, the device provides information used to help collect other signals that change with respiration, such as breath sounds, cardiac information, radiation detection devices, radiation therapy devices, ablation devices, and so on. ..

In a preferred embodiment, the device can assist in timing or data collection by another modality and/or by using the characteristics of the respiratory curve to correct the collected data.

In one embodiment, the device provides information about breath-to-breath variability or respiratory complexity to be used with beat-to-beat variability or complexity, including cardiac status, pulmonary system status. , Or provide information about the overall metabolic or neurological status not otherwise available.

Lead Configuration The proposed respiratory parameter estimation technique relies on a highly linear relationship between the parameter and the measured impedance. This does not apply to all electrode installations. Extensive research has been carried out to select the best electrode placement, which preferably satisfies the following conditions:
1) There is a highly linear relationship between respiratory volume and measured impedance variation (ie a correlation value above 96%).
2) Low level of artifacts due to patient movement.
3) Low variability between repeated electrode applications.
4) It can be easily applied in general clinical situations.
Ability to use with "Universal Calibration", "Universal Calibration" reliably determines scaling factors depending on measurable patient body parameters without preliminary calibration with a ventilator/spirometer To do.

Preferably, the electrodes are mounted horizontally with respect to the mid-axillary line at the level of the sixth rib. Preferably, one electrode is placed in a stable location, for example, just below the clavicle or in the sternal notch, and another electrode is placed at the bottom of the rib cage or at the sword in the mid-axillary line. Installed at the level of the ridges. However, the electrodes may be placed higher or lower in the rib cage. Moreover, depending on the subject to be tested, the test to be performed, and other physiological concerns (eg, whether the patient has a pacemaker or other artificial device). , The electrodes may be placed elsewhere and in other configurations (eg, vertically along the rib cage, at an angle across the rib cage, or from a position in front of the patient to a position on the patient's back).

Preferably, at least one impedance measuring element is present on one or more electrode leads. Preferably, the two or more electrodes are arranged in a linear array, in a grid pattern or in an anatomically affected configuration. Preferably four remote probes are arranged in a linear array. In another embodiment, multiple electrode leads are arranged as a net, vest, or array. Preferably, one or more probes, electrode leads, or sensors are placed on the subject's thorax or abdomen. Preferably, the device uses single-use electrodes. In other embodiments, the electrodes are hydrogels, hydrocolloids, or solid gels. Preferably, the electrodes utilize AgCl, nickel, or carbon sensors. Preferably, the electrode comprises a soft cloth, foam, microporous tape, clear tape backing, or another adhesive. Preferably, for adults and newborns, there are suitable electrodes of different sizes, the adult electrode being larger than the newborn electrode, which is preferably less than 1" x 3/8" (2.54 cm×0.95 cm or less). In other embodiments, the sensor electrode is the same as the probe that delivers the electrical impulses to the body, is different from the delivery electrode, or is wireless and sends data to the remote sensor. In another embodiment, the delivery probes are themselves sensors. In one embodiment, the stimulation electrodes are battery powered. Preferably, the at least one respiratory parameter is maximally continuous, intermittently, for a duration of 30 seconds, and at most for at least 3, 5, 10, 20, or 50 breaths of the subject. At least 100 breaths of the subject, up to at least 1000 breaths of the subject, or for another duration. Preferably, the subject's impedance electrocardiogram is recorded at the same time.

Preferably, the at least one impedance measuring element comprises one or more remote probe or electrode leads, or is similar to a standard EKG lead or is used to measure cardiac impedance. The programmable element is further programmed to analyze one or more remote probe or electrode lead data sets collected from the one or more remote probe or electrode leads, including a lead wire similar to the lead wire. There is.

In one embodiment of the invention, the impedance measurement subsystem reads impedance from multiple channels. In a preferred embodiment, the secondary voltage sensing channel is arranged at an angle to the primary voltage sensing channel. In one embodiment, the two channels share a current generating electrode. In one embodiment, the two channels also share one of the voltage sensing electrodes. Data from more than one channel can be used in an adaptive algorithm to determine and suppress noise from motion.

Lead configuration, in any embodiment, is important to device performance. Preferably, one or more leads are placed on the rib cage. In one embodiment, leads are placed over the rib cage and abdomen to measure respiration from different areas of the body, such as the rib cage or abdomen. Differences in the location of body movements associated with breathing produce clinically useful information regarding diagnosis of physiological conditions and monitoring of disease, which can be compensated for in the calculations. The leads are placed over the rib cage, neck, and head in an alternative configuration. In one embodiment, the leads are placed in different configurations based on anatomical location and spaced according to either a particular measured distance or anatomical landmark, or a combination of both. To be placed. In one embodiment, a modification of the spacing to body size is implemented. Preferably these modifications are associated with anatomical landmarks. In a preferred embodiment, the spacing remains relatively the same for patients of all sizes, from neonatal to obese patients, ranging from 250 g to 400 kg. In another embodiment, the spacing varies based on an algorithm that reflects body size and body shape. Other configurations have the advantage of determining the difference in movement between one hemi-thorax and the other, which is unilateral or asymmetrical, such as pneumothorax, hemothorax, empyema, cancer, etc. It is useful in diagnosing or monitoring lesions in.

Referring now to FIG. 2, there is shown one embodiment with a particular electrode configuration referred to as posterior left to right (PLR), where the posterior left and right (PLR) is An electrode 7 is placed 6 inches to the left of the spine at the level of the xiphoid process, and a second electrode 8 is placed 2 inches to the left of the spine at the level of the xiphoid process. Electrode 9 is placed 2 inches to the right of the spine at the level of the xiphoid process, and fourth electrode 10 is placed 6 inches to the right of the spine at the level of the xiphoid process. The advantage of placing the electrodes in this configuration is that both lungs are woven to a high level of signal reading.

Referring to FIG. 3, there is shown a second particular electrode configuration called posterior vertical right (PVR), where the posterior vertical right (PVR) shows that the first electrode 11 is , Located just below the scapula, midway between the mid-axillary line and the spine, the second electrode 12 is located 2 inches below electrode 1, and the third 13 electrode is , 2 inches below the electrode 2 and a fourth electrode 14 below the electrode 3. The advantage of this configuration is reduced electrode movement due to thoracic expansion and less heart interference. This location has the benefit of little or no volume change between the electrodes and the benefit of less heart noise.

Referring to FIG. 4, there is shown a third specific electrode configuration referred to as an anterior to posterior (AP), where from anterior to posterior (AP), the first electrode 15 is a sword. Located 6 inches to the right of the right mid-axillary line at the level of the trichome, and the second electrode 16 is located 2 inches to the right of the right mid-axillary line at the level of the xiphoid process; Electrode 17 is placed 2 inches to the left of the right midaxillary line at the level of the xiphoid process, and a fourth electrode 18 is placed 2 inches to the left of the right midaxillary line at the level of the xiphoid process. Has been done. This position captures the largest volume, which is useful for determining respiratory localization.

Referring to FIG. 5, there is shown a fourth specific electrode installation called the Anterior Vertical Right (AVR), in which the first vertical electrode 19 is the sword. Located midway between the xylem and mid-axillary line, just below the clavicle and with a third electrode 20 aligned with the first electrode and at the level of the xiphoid process, A second electrode 21 is placed 4 inches above the third electrode and a fourth electrode 22 is placed 4 inches below the third electrode. This position is useful for newborns and for other patients whose characteristics prevent the operator from placing the leads on the posterior side. The positions of the other four probes are placed equidistant from each other or at clearly measured distances, above the abdomen and rib cage, vertically and horizontally. The probe location is also located at physiological landmarks such as the iliac crest or the third intercostal space. Placing the probe on both the abdomen and rib cage allows the relationship between chest and abdominal breathing to be determined. This relationship supports the diagnosis and monitoring of therapy.

In addition to the four probe configurations described above, these configurations add probes equidistant between positions, for example, in the AP configuration, between electrode 1 and electrode 2, between electrode 2 and electrode 3. During, between electrodes 3 and 4, two inches from each electrode, can be modified to include more probes by adding electrodes in line with the installation. With multiple electrodes, they can be placed in a grid pattern equidistant from each other; this configuration will be discussed further below. Other placements for more than one lead include around the rib cage at equidistant points at constant height, such as xiphoid. A particular installation for a 24 lead system is in a linear array with 12 leads, each linearly equally spaced on the chest and back. Such a lattice or array may be realized in a net or vest to be worn by the patient. In one embodiment, the device provides a table that describes alternatives to lead placement and provides a measurement device to aid in probe placement. In one embodiment, the measured distance between the leads is automatically ascertained by the leads, the leads being the positioning sensor and/or the distance from one sensor to one or more other sensors. It has a sensor that can determine

Referring now to FIG. 6, a number of electrode configurations 23 are shown, some of which are connected to each other by an analog multiplexer 24 and are programmable by a radio frequency impedance meter 25 and a PC or the like. It is connected to the element 26. Shown are embodiments of devices that implement the lead and multiplexer configurations shown in the previous figures, ie, FIGS. 2 and 3. In FIG. 6, each lead is connected by a multiplexer to several different electrodes. The advantage of this configuration is that it allows the device to digitally switch the electronic inputs and outputs of the DAS and effectively switch the electrode configuration to collect data about impedance in several directions at about the same time. Is. For example, a twelve-electrode system is made up of four different sets of leads, with the first set going to the corresponding first electrode in each configuration and the second set of leads. Goes to the corresponding second electrode in each configuration, and so on.

The electrode configurations are also made to correspond to anatomical positions on the rib cage, abdomen, and limbs, such as the rest ICG position shown in FIG. 7, where the first electrode 27. Is placed above the forehead, the second electrode 28 is placed above the left clavicle, and the third electrode 29 is placed above the mid-axillary line at the level of the xiphoid process. Therefore, the fourth electrode 30 is placed above the mid-axillary line just above the iliac crest.

Each electrode configuration will be affected by motion in a different manner. For example, movement of the right arm can cause motion artifacts on any lead placement that traces impedance across the right pectoral muscle, latissimus dorsi, trapezius, and other muscles of the chest and upper back. become. By noting the differences between the shapes, derivatives, or magnitudes of signals recorded simultaneously from different lead placements, local motion artifacts can be identified and subtracted from the impedance signal.

In one embodiment, the probe is manufactured in a linear strip, with the linear strip having a fixed distance between the delivery and the sensor electrode with a delivery and sensor pair at each end. However, a separate pad is formed. In the preferred embodiment, there is a compliant strip between the two pads, which can be stretched to allow specific positioning for the appropriate patient based on anatomical landmarks. Preferably, the material will retain its stretched configuration when stretched.

Probes Referring now to FIG. 23, an embodiment of a device is shown in which one or more remote probes, which are embodied as surface electrodes, speakers, and/or microphones, It is integrated into a vest 46 which is connected to an impedance plethysmograph 47 using a cable. The advantage of this embodiment is that the positions of the leads are determined by the best manufacturer and thus they are standardized. That is, use of the vest eliminates operator error regarding lead configuration. In an alternative embodiment, the probe and actuator are wireless. In an alternative embodiment, the vest also includes leads that cover the abdomen.

Referring now to FIG. 24, an embodiment of the device is shown in which one or more remote probes are integrated into the array 48 and the electrodes are gently placed on the patient's skin. It is connected by a soft piece of cloth or mesh that is pressed against it. The benefit of this configuration is that the inter-electrode distance is standardized by the array manufacturer, thus reducing operator-dependent error on the electrode configuration.

Referring now to FIG. 25, an embodiment of a device is shown in which one or more remote probes are connected to each other by a string and can be rapidly and effectively applied to the patient's skin. Forming 49. The benefit of said embodiment is that the inter-electrode distance and the relative position of the electrodes with respect to each other are standardized, thus reducing the effect of operator-dependent errors. In another embodiment, the elastic elongation of the string provides probe adjustment for different body shapes. Preferably, the stretchable material will provide distance measurements either by being read on the material or relaying information about the stretch to the device. Preferably, the strings are displacement sensors or strain gauges, such as linear displacement transducers, etc., which are operatively connected to programmable elements to relay information about the length that each string of the net is stretched. Will be installed. Preferably, the programmable element is further programmed to account for changes in the lead placement relayed to it from the displacement sensor.

Referring now to FIG. 26, an embodiment of a device is shown in which one or more remote probes are operably connected to a remote transmitter 50 and a programmable element 51 is a remote receiver. It is connected to the. Proposed communication protocols for systems range from limited scope to vastly networked systems of several nodes. It provides the basis for an unlimited number of use cases. In one embodiment of the telecommunications protocol, a short range high frequency system such as Bluetooth v4.0 is used. This emulates a wireless solution of what an RS-232 wired connection would provide. This enables communication between two devices in close proximity quickly and securely. In another embodiment, a generally 802.11 compliant protocol is used to generate a mesh network of closest devices. This mesh network incorporates all of the devices in a given unit. Unit size is not bounded because the addition of individual nodes increases range (since the network is configured and governed by the nodes themselves-no underlying infrastructure is required, range and unit size are directly Proportional). Only huge outliers leave this network. This means that in order for the outliers to be omitted, the nearest node currently connected must be clearly out of range for the outliers to communicate. These services, specifically the hardware, can run/poll without using the main CPU (minimize battery use). This is useful because it only acts as a relay node when the device is not being read. The nature of the system minimizes power requirements (increases service life), supports asymmetrical links/paths, and allows each node to perform multiple roles to benefit the network.

Another embodiment requires a connection to a LAN or WAN network and the remote procedure is catalyzed by a user-driven event (such as pressing a button). This will generate a unique identifier for digital reception of the data transaction on each phone that is concatenated with device specific information. This information is supplemented by the GPS location to distinguish the location of the device. Since the data transmission is initiated by both parties at the correct time coupled with GPS information, the system can securely identify both parties by location, UID, and device identifier. All methods are secured by anonymity heuristics and encryption. This will prevent snooping of data, a problem presented by "man in the middle" attacks.

Another embodiment of the device utilizes one or more electrical probes implanted in the body. In one embodiment of the invention, the implanted probe is connected to a cardiac pacemaker. In another embodiment, the implanted probe is connected to an internal automated defibrillator. In another embodiment, the implanted probe is connected to the phrenic nerve stimulator. In another embodiment, the implanted probe is connected to a delivery pump for analgesics, local anesthesia, baclofen, or other drugs. In another embodiment, the implanted probe is connected to another implanted electronic device. Preferably the connection is wireless.

Referring now to FIG. 33, the electrode configuration XidMar is shown. Configuration XidMar is a two channel configuration with electrode 1 above the xiphoid process and electrode 4 horizontally aligned with electrode 1 and above the right mid-axillary line. .. Electrode 2a is 1 inch to the left of electrode 1, while electrode 3a is 1 inch to the right of electrode 4. Electrodes 2a and 3a are used to record the voltage signal on channel a. Channel b is recorded using electrodes 2b and 3b, electrodes 2b and 3b are found 1 inch below the corresponding electrode of channel a.

FIG. 34 shows a StnMar electrode configuration, where electrode 1 is located directly below the sternal notch and electrode 4 is horizontally aligned with the xiphoid process in the right middle region. Located above the axillary line. Electrode 2a is located 1 inch below electrode 1 and electrode 3a is located 1 inch to the right of electrode 4. Channel b is at an angle of approximately 45 degrees to channel a. Electrode 2b is located above the xiphoid and electrode 3b is located 1 inch below electrode 3a.

FIG. 35 shows the StnIMar electrode location, where electrode 1 is located just below the sternal notch, and electrode 4 is at the bottom of the rib cage at the lower right midaxillary line. Located on top of. Electrode 2a is located 1 inch below electrode 1 and electrode 3a is located 1 inch to the right of electrode 4. Electrode 2b is located above the xiphoid and electrode 3b is located 1 inch below electrode 3a.

FIG. 36 shows a McrMar electrode configuration, where electrode 1 is located just below the clavicle, above the right midclavicular line, and electrode 4 is horizontally aligned with the xiphoid process. Located on the right mid-axillary line. Electrode 2a is located 1 inch below electrode 1 and electrode 3a is located 1 inch to the right of electrode 4. Electrode 2b is located above the xiphoid and electrode 3b is located 1 inch below electrode 3a.

FIG. 37 shows an McrIMar electrode configuration, where electrode 1 is located just below the clavicle and above the right midclavicular line, and electrode 4 is in the lower central axilla approximately at the bottom of the rib cage. Located above the line. Electrode 2a is located 1 inch below electrode 1 and electrode 3a is located 1 inch to the right of electrode 4. Electrode 2b is located above the xiphoid and electrode 3b is located 1 inch below electrode 3a.

FIG. 38 shows a MclMar electrode configuration, where electrode 1 is located just below the clavicle and above the left midclavicular line, and electrode 4 is horizontally aligned with the xiphoid process. Located on the right mid-axillary line. Electrode 2a is located 1 inch below electrode 1 and electrode 3a is located 1 inch to the right of electrode 4. Electrode 2b is located above the xiphoid and electrode 3b is located 1 inch below electrode 3a.

The electrode configurations shown in Figures 34-38 can utilize either channel a or channel b, or both simultaneously to measure data.

In one embodiment of the invention, the system is adapted to perform an impedance tomography scan utilizing one or more pairs of source electrodes and one or more voltage sensing electrodes. The scan is completed by taking a series of measurements with movable electrodes applied to the skin. The movable electrode, together with at least one other electrode, forms a voltage measuring pair for impedance reading. The movable electrode can be coated with a hydrogel and the hydrogel can be applied multiple times. In another embodiment of the invention, the electrodes house a hydrogel dispenser for each application. In this embodiment, the hydrogel is stored in an internal pouch or syringe and there is a device, such as a mechanical button or squeeze tube, which allows the user to dispense the hydrogel onto the electrode. Allows you to make a note. In one embodiment of the device of the present invention, the system allows the user to sweep the movable electrode between predetermined points on the body, such as shown on the user interface or on the reference card. Instruct. In another embodiment, the user can install movable electrodes at each point and the system uses a camera, sonar, radar or other device to sense the location of the electrodes.

The firm adhesion of the electrodes determines the quality of the impedance reading. In one embodiment of the invention, the system detects the quality of the bond and reports the bond index to the user. In another embodiment, the system reports a problem with gluing if the index exceeds a certain threshold. In the preferred embodiment of the present invention, there are multiple voltage sensing channels arranged in a straight line. This can be achieved using 5 electrodes arranged in a row. Referring to the five electrodes by letter, electrodes A and B are located near each other at one end of the line and electrodes D and E are located near each other at the other end of the line. .. Pairs AB and pairs DE can be placed 3-24" away from each other. Electrode C is placed somewhere between the two pairs. Impedance is 3 channels, B- Measured on C, C-D, and B-D. If all electrodes are well adhered, the sum of Z _BC and Z _CD should be close to Z _BD . The difference between the measurements or the ratio of the total measurements can be used to determine the index of bond quality.

In one embodiment of the invention, electrode C is not in line with the other pair of electrodes. In this case, the impedance is measured on channels BC and BD. The ratio between the impedances Z _BC and Z _BD on the two channels is used to determine the index of adhesion quality. In another embodiment of the invention, the current driven through electrodes A and E is measured. Amperage measurements, or variability of amperage measurements, can be used to determine the index of adhesion for electrodes A and E.

Electrical connectors have an inherent capacitance that can affect impedance measurements. In one embodiment of the invention, the system compensates for the capacitance of cables, leads, or other electrical connections between the impedance measurement subsystem and electrodes connected to the patient. In one embodiment, this is accomplished by an inductor in the impedance measurement subsystem. In another embodiment, the compensation inductor is integrated into the patient cable or lead that connects the impedance measurement subsystem to the electrode pads connected to the patient. In another embodiment, the compensation inductor is embedded in the integrated electrode PadSet. In another embodiment, a modification of the Howland circuit consisting of capacitors C ₁ and C ₂ with values selected to compensate for the parasitic capacitance Cc is used (see FIG. 39).

To achieve high clinical relevance and a good definition of respiratory curves, the impedance measurement subsystem can determine small variations in patient impedance above a relatively high baseline background with high resolution. Should be. Therefore, there are stringent requirements regarding absolute and relative impedance measurement errors. In order to obtain sufficient accuracy, one or more of the following design solutions can be used: (1) The electronic design can be based on high precision/low temperature drift electronic components. (2) A precision analog divider can be used to obtain the ratio between the measured voltage and the monitored source current, compensating for variations in the source current; (3) same. Voltage can be used for source current generation and as an ADC reference to compensate for variations in reference voltage; (4) External calibrated impedance standard to calibrate and verify impedance measurement subsystem performance. Can be used for. The calibrated system is preferably connected to an impedance standard by the same mains cable used for patient measurements to provide verification of overall system performance. (5) The impedance measurement subsystem can have a built-in calibrated impedance standard, allowing on-site verification and recalibration. In one embodiment, the self-contained standard is attached to the system via the external service port. Calibration is accomplished by connecting the "patient" end of the mains cable back to the service port on the device and by running the calibration procedure available through the GUI of the device. (6) Calibration can be completed by varying the built-in standard impedance and deriving a device model over the entire range of measured patient impedance, which is measured during patient measurement. Used to achieve high precision results. (6) A temperature model of the device can be derived by placing the device in a thermostat and measuring the drift in the measurements depending on the internal device temperature. Internal device temperature can be monitored via a built-in thermal sensor. During patient measurements, measurement corrections are calculated using the thermal sensor readings and applied to the measurements.

Active Acoustic System For acoustic measurement of lung volume, the device preferably comprises at least one speaker and at least one microphone. Preferably, at least one speaker and microphone are arranged as a net, vest or array. Preferably, at least one speaker switches between discrete frequencies or emits broad spectrum noise. Preferably, multiple speakers are active at the same time and emit different acoustic signals. Preferably, multiple microphones are active at the same time to record the measured rib cage acoustics, which can be correlated to lung volume and lung lesions. Preferably, the microphone also records sounds emanating from within the lungs, such as wheezing, squarks, and crackles, which can be indicators of numerous chronic and acute lung diseases. is there. Preferably, lung sounds are recorded and identified as they are modified by the active signal. Preferably, the algorithm analyzes the number and location of wheezing, squawks, and crackles to predict asthma and other lung diseases. In one embodiment, acoustic data is combined with impedance data to help time the acoustic measurements with respect to the respiratory cycle. In one embodiment, acoustic data is combined with impedance data for purposes of disease diagnosis or monitoring. An example of this is congestive heart failure, where stiffness produces a characteristic change in the impedance curve, and there are also changes in pulmonary sounds associated with congestive heart failure. The combination of data provides additional information.

Referring now to FIG. 20, a device is shown in which a speaker 38 is attached to the patient's chest and is insulated by a sound attenuating foam 39. The microphone 40 is mounted on the patient's back and is insulated by sound attenuating foam. Both the speaker and the microphone are functionally connected to the programmable element 41, for example a computer with installed analysis software such as MATLAB or the like. The output element provides the operator with data regarding the breathing of the patient in real time. The speaker produces an acoustic signal, which is recorded by the microphone. Signal generation and recording are timed and synchronized by programmable elements. The analysis software uses the recorded sound wave characteristics to evaluate the acoustic properties of the rib cage, which can be used to estimate lung volume. The signal features include, but are not limited to, frequency dependent phase shift and amplitude attenuation. Preferably, the speaker switches between distinct frequencies of the sound or produces broad spectrum white noise.

In another embodiment of the device, the microphone is also used to detect sounds that occur in the lungs, such as crackle, skuwalk, wheezing and the like. In one embodiment, the programmable elements of the device will use software algorithms to detect the relevant acoustic patterns and inform the physician. In one embodiment, the acoustic system will also interface with impedance-based systems.

Referring now to FIG. 21, an embodiment of the device is shown in which an array of microphones 42 is used to record transmitted sound from different areas of the rib cage. Preferably, the microphones record simultaneously. Preferably, programmable element 43 selects the microphone with the best signal to noise ratio for analysis. Preferably, the programmable element combines data from different channels to maximize the accuracy of lung volume estimation and to locate lung lesions, including tumor formation, hemorrhage, and tissue deterioration.

Referring now to FIG. 22, an embodiment of the device is shown in which an array of speakers 44 is used to generate sound waves. Preferably, the programmable element 45 controls each of the speakers individually and also switches between the speakers to allow the device to measure the acoustic characteristics of the rib cage in many different directions. Preferably, the programmable element will simultaneously activate each speaker with a signal of a unique frequency, such that the signals from each speaker can be separated in the recorded signal. Preferably, the programmable element combines data from different channels to maximize the accuracy of lung volume estimation and to locate lung lesions, including tumor formation, hemorrhage, and tissue deterioration.

Entering Patient Data Preferably, the device software maintains a user friendly GUI (Graphical User Interface). Preferably, the GUI houses a color coding system to assist the operator in making quick diagnoses and making decisions for patient care. In one embodiment, the GUI presents a numerical RVM measurement. In one embodiment, the GUI presents a respiratory satisfaction index (RSI). In one embodiment, the GUI presents the respiratory waveform.

In the software present on all embodiments of the device, patient data is preferably recorded by the user prior to the examination. The user is prompted to enter patient data. Data recorded include any or all of the following: patient height, weight, chest at maximal inspiration, chest at end-tidal normally, age, sex, ethnicity, and smoking history. In one embodiment, the pose at the time of examination is also input into the device in the programmable GUI. Postural variability can lead to different breathing patterns and tidal volumes. The device accepts posture inputs, such as supine, sitting, and standing. Being able to test patients in multiple postures is useful in non-compliant patients, such as neonates or debilitated patients.

In one embodiment, the device calculates BMI. In a preferred embodiment, an algorithm in the device or on a look-up table calculates a "calibration factor" which corrects for patient size and body shape to provide a universal calibration, Deliver absolute measurements. The calibration factor can be obtained by combining the patient information with the data recorded from the applied probe. Preferably, the physical location of the probe is also entered. During data acquisition, the calibration algorithm is capable of demonstrating consistency with the data and its input patient information, and may suggest the most consistent combination of input parameters with the recorded data. It is possible, and it is also possible to instruct the operator to recheck the patient information. As the data is acquired, the calibration algorithm can suggest and/or perform readjustment based on the signal pattern recorded from the probe and/or provided by the operator as normal or abnormal. Is. In another embodiment, the device calculates BSA or another index of body shape or size. In one embodiment, the system displays a patient outcome predictor based on the patient data described above. In one embodiment, the device also provides percentage comparisons for these values in the displayed results, based on standard tables of spirometry data generated by Knudsen, Crapo, or others. To further inform the clinician of patient parameters or conditions. In one embodiment, patient demographics and/or body measurements are entered and the device is configured for lead configuration and/or lead spacing and/or lead size for the patient. Or suggest a characteristic.

In one embodiment, the device assesses signal variations and adjusts display parameters, calibration parameters, and/or intermediate calculations in response to the variations. In one embodiment, the device comprises a baseline, average, minimum, maximum, dynamic range, amplitude, rate, depth, or second or third derivative of any item in the list, Assess variations in one or more features of the signal.

In one embodiment, the device calculates a calibration factor for converting raw or processed impedance traces into respiratory volume traces. In one embodiment, the calibration factor is calculated from a range of physiological and demographic parameters. In one embodiment, the device of the present invention automatically adjusts the calibration factor in response to parameter variations. In one embodiment, the device automatically adjusts the calibration factor in response to one or more of: respiratory rate, baseline impedance, or average impedance.

In one embodiment, the device includes one or more of respiratory rate, baseline impedance, or average impedance in the calculation of the coefficient, or a correction factor for the calibration coefficient. In embodiments where the calibration factor is based on a time-varying parameter, such as respiratory rate, baseline impedance, or average impedance, the device automatically adjusts the calibration factor to account for parameter variations. To do.

In one embodiment, the device adjusts the calibration factor based on the assessment of signal variation. In one embodiment, where the calibration factor is used to transform the raw impedance signal into a respiratory volume trace, the calibration factor is based in part on the respiratory rate.

In one embodiment, the device adjusts the display of the dataset in response to variations in the dataset. The data set consists of raw signals from the sensor, processed signals from the sensor, or calculated metrics or parameters.

In one embodiment, the device adjusts the y-axis minimum above the displayed chart in response to variations in the data set. In one embodiment, the y-axis minimum above the displayed chart is equal to the dataset minimum. In one embodiment, the minimum of the y-axis above the displayed chart is equal to the minimum of the dataset in the particular window. In one embodiment, the window in which the associated minimum of the dataset is calculated is the same window in which the data is displayed. In one embodiment, the minimum on the y-axis above the displayed chart is equal to the minimum of the dataset in the display window minus the coefficient or percentage of the minimum.

In one embodiment, the device adjusts the y-axis range of the displayed dataset to account for variations in the dataset. In one embodiment, the y-axis range of the displayed dataset is equal to the dynamic range of the dataset. In one embodiment, the y-axis range of the displayed dataset is equal to the dynamic range of the dataset in the particular window. In one embodiment, the y-axis of the displayed dataset is equal to the dynamic range of the dataset in a particular window plus a constant or percentage of the dynamic range.

In one embodiment, the device adjusts the y-axis range of the displayed dataset based on the statistics of the characteristics of the dataset. In one embodiment, the device sets the y-axis range to be equal to the average amplitude of the signal, multiplied by a factor, plus the standard deviation of the amplitude of the signal within a particular window. .. In one embodiment, the device causes the y-axis of the displayed data set to be equal to the average amplitude of the signal times the coefficient times the variance of the amplitude of the signal in the particular window. Adjust the range of. In one embodiment, the device calculates the amplitude of respiration in the dataset. The device then removes the outliers at the high end, the outliers at the low end, or outliers with features that appear to be unrelated to the intended parameter being measured. The device then adjusts the range of the y-axis so that it is equal to the average amplitude of the data set, multiplied by a factor, plus the standard deviation of the data set.

In one embodiment, the device automatically adjusts the midpoint of the y-axis of the data set's chart in response to changes in the data set. In one embodiment, the device sets the y-axis to be equal to the average of the data sets in a particular window. In another embodiment, the device sets the y-axis to be equal to the median of the dataset in the particular window. In one embodiment, the device sets the midpoint of the y-axis to the result of a function of the data set's statistics.

Calibration Method The calibration factor is calculated in a novel way. In the preferred embodiment, the device contains circuitry and software that automatically calibrates the device. In one embodiment, the calibration is aided by data acquired through bioelectrical impedance analysis, which is a process of measuring tissue impedance above one or more channels at various frequencies. In this embodiment, the data from the bioelectrical impedance analysis can be used to calculate certain characteristics of the subject, including, but not limited to, water content level, baseline impedance, and body composition. Low levels of water make the body's electrical impedance greater. High levels of fat in the body will also cause an increase in the body's average electrical impedance, but it is also likely to cause a decrease in overall impedance as electricity passes through the path of least resistance. .. Muscle is much more vascular than fat and contains more conductive electrolytes, so the body of a muscular patient has a much lower electrical impedance than a less muscular, similarly sized person. Will have. Scaling the calibration factor based on these inputs makes the calibration factor more accurate.

Calibration of the device of the present invention preferably includes predictions regarding respiratory rate, tidal volume, and minute ventilation, based on metabolic requirements of body tissues. Prediction preferably involves multiplying the measured or ideal weight of the patient by the volume of air required by the unit weight, or the volume of air per minute. Ideal weight is determined from the height, race, and/or age of the patient, and may further be determined by one or more of the Devine, Robinson, Hamwi, and Miller formulas.

In one embodiment, the calibration factor is calculated from patient demographic information including, but not limited to, sex, age, and race. In another embodiment, the calibration factor includes, but is not limited to, a physiological measurement of a patient, including, but not limited to, body type, height, weight, chest circumference, body fat percentage, body surface area, and body mass index measured at different points in the respiratory cycle. Calculated from the value. In another embodiment, the calibration factor is calculated based on measurements of ECG signals recorded at different points. More specifically, ECG is recorded by electrodes at various locations on the rib cage and abdomen. In one embodiment, the recording of the differential voltage at the different electrodes is used to calculate the average baseline impedance and to estimate the patient's rib cage resistivity in various directions. In another embodiment, the calibration factor is a two-pole configuration, a four-pole configuration, or other configuration that includes more than one lead, in a patient baseline to an external current source, as measured between electrodes. It is calculated based on the impedance. The locations of these electrodes are located in a range of configurations throughout the body. In another embodiment, demographic characteristics are combined with baseline impedance measurements for calibration. In another embodiment, anatomical information is combined with baseline impedance measurements for calibration. In a preferred embodiment, the known volume recorded on the spirometer or ventilator is combined with demographic information and baseline impedance. In such an embodiment, the system is capable of measuring impedance (using a spirometer, ventilator, or other similar device) and volume simultaneously. The system then calculates the particular transformation between impedance and volume as an input to the transformation algorithm.

In another embodiment, the overall patient impedance (including skin impedance and fat layer impedance), internal organ impedance (baseline impedance), and variations thereof, and respiratory, obtained using an impedance measurement subsystem, are described. A dynamic calibration based on an additional parameter, consisting of a curve shape, is realized.

A continuous or intermittent calibration check is preferably performed. In the preferred embodiment of the device, the calibration is recalculated at each sample record. In another embodiment, the device is recalibrated periodically based on the timer function. In another embodiment, the device is recalibrated whenever the baseline impedance changes from the baseline by a certain threshold (eg, 10%, etc.). In another embodiment, whenever the tidal volume or minute ventilation changes from a baseline or predicted level by a certain threshold (such as 20%), the device is recalibrated, where the predicted value is Is calculated using the formula published by Krappo, Knudson, and others.

A continuous or intermittent calibration check can be performed. Preferably, this involves an internal check to an internal phantom.

Preferably, a continuous or intermittent baseline impedance check is used to recalibrate or revalidate the calibration. Preferably, continuous or intermittent readings from each hemithoracic are used individually or in combination to recalibrate, or used to provide data for recalibration. To be done.

Preferably, the recalibration is performed automatically or is an addition to be taken by the caregiver by alerting the caregiver of the required correction, or by recalibration with a ventilator or spirometer, for example. It is carried out by requesting specific steps.

In one embodiment, the calibration is done through the measuring electrode pair. In another embodiment, the calibration is done through additional electrodes. In another embodiment, the calibration is performed wholly or partially by using the measurement electrode for another purpose and by using the sensor as a delivery electrode and the delivery electrode as a sensor electrode. ..

Preferably, the calibration electrodes are placed at specific locations on the abdomen and thorax and/or at specific distances apart. In another embodiment, one or more of the leads are placed a certain distance above the forehead. In another embodiment of the device, the magnitude of the ICG signal across the acceptable electrode configuration, with or without estimation of the heart volume, determines the baseline impedance, Used to calibrate RVM data for respiratory volume. Preferably, the calibration factor is calculated using a combination of the five previously mentioned methods.

Universal Calibration The relationship between respiration and impedance variation is highly linear, but the "scaling factor" between those values varies significantly from patient to patient. There are also daily variations for the same patient. Day-to-day variations can be correlated to some extent with physiological parameters measured by the RMV device and can be significantly compensated. The residual daily variation for the same patient is less than the typical measurement error. In the preferred embodiment, this residual variation can be managed by existing ancillary measurements. In a preferred embodiment, this residual variation can be managed using continuous or intermittent recalibration by any of the previously described methods.

In one embodiment, the "scaling factor" varies from patient to patient by about an order of magnitude. In the preferred embodiment, this factor can be precisely determined by preliminary calibration with spirometer or ventilator data or other data sets. In the preferred embodiment, the RMV device is used for measurement of respiratory parameters without preliminary calibration. Preferably, a reliable procedure for inferring this factor from measurable patient physiological parameters is used for the calibration. Such a procedure allows the determination of "scaling parameters" with sufficient accuracy to meet the measurement requirements for all of the proposed device applications.

In one embodiment, derived from techniques including impedance plethysmography, accelerometers placed on the body, video images, acoustic signals, or other means of tracking movement of the rib cage, abdomen, or other body part. The respiratory movement measurements made are calibrated or correlated by another technique for assessing respiratory status. In a preferred embodiment, respiratory motion detection derived from impedance measurements is calibrated by spirometry. In one embodiment, respiratory motion detection is calibrated or correlated by end tidal CO2 measurements. In one embodiment, respiratory motion detection is calibrated or correlated by flow and/or volume ventilator measurements. In one embodiment, respiratory movements are calibrated by a whole body plethysmograph. In one embodiment, a baseline RVM measurement for a given patient is taken with a standard spirometry measurement to derive a calibration factor for that particular patient. Later, in the post-operative period, or otherwise, the calibration factor is used to obtain a quantitative lung volume measurement for the patient. In the preferred embodiment, such calibration factors are combined with current baseline impedance or other physiological measurements for continuous or intermittent calibration. In one embodiment, pre-surgery measurements are used to derive the calibration factor, which is then used alone or in combination with other data to obtain quantitative lung volume measurements. And in the management of patients after surgery or in other situations. In another embodiment, the calibration factor is derived from lung volume or flow measurements taken for the patient being intubated, from measurements recorded from a mechanical ventilator.

Preferably, the device is linked to a spirometer, ventilator, or pneumotachometer to provide volume or flow calibration. Preferably, the device is linked to a spirometer, ventilator, or pneumotachometer to provide volumetric calibration. In one embodiment, the operator is: at least one tidal breathing sample, at least one forced vital capacity (FVC) sample, at least one measurement of the minute ventilation sample, and at least one maximum forced ventilation volume. Patients will be passed a brief respiratory test regimen for one or more of the (MVV) samples. The device will be calibrated based on the results of the spirometer test in connection with the impedance measurement. In the preferred embodiment, the calibration will be realized from measurements taken during a single breath. Among other things, for patients who are unable to follow the procedure, a simple tidal breath sample will be taken, which does not require coaching or compliance. The tidal breath sample is collected for 15 seconds, 30 seconds, 60 seconds, or another time frame.

In one embodiment, a calibration factor for a given individual is calculated based on the combined spirometry and RVM data and applied to deliver absolute volumetric measurements for RVM measurements made at future time points. It Preferably, this absolute volume measurement will be validated or modified at a future point in time using hardware specific calibration capabilities and current measurements derived from the device. In a preferred embodiment, existing normal spirometry data for various patient demographics found in patient demographics, Knudsen, Crapo, and others, and/or other anatomical or physiological measurements. Based on the values, an algorithm can be applied to the RVM data to provide a universal calibration and deliver absolute volumetric measurements without the need for individual calibration with a spirometer or ventilator. is there.

Preferably, the device is used in conjunction with ECG or ICG data to create additional calibrations of impedance data by utilizing ECG and ICG derived parameters such as heart rate and SNR. Is. Preferably, ECG or ICG data will help demonstrate proper electrode placement. In another embodiment, cardiac electrical activity is used to enhance device calibration. Preferably, the device is of the following cardiac, pulmonary, and other physiological parameters and characteristics: heart rate (HR), baseline impedance, impedance magnitude, pre-ejection period (PEP), left ventricular ejection. Time (LVET), systolic time ratio (STR), stroke volume (SV), cardiac output (CO), cardiac index (CI), thoracic water content (TFC), systolic blood pressure (SBP) , Diastolic blood pressure (DBP), mean arterial pressure (MAP), mean central venous pressure (CVP), total peripheral vascular resistance (SVR), myocardial work (RPP), heather index (HI), stroke volume index (SVI) and waveform accuracy value (WAV) can be measured. Baseline values calculated from patient characteristics for these features are used to derive calibration factors and to calculate an index of overall respiratory satisfaction. Conversely, RVM data includes heart rate (HR), baseline impedance, impedance magnitude, pre-ejection period (PEP), left ventricular ejection time (LVET), systolic time ratio (STR), stroke volume ( SV), cardiac output (CO), cardiac index (CI), intrathoracic water content (TFC), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), mean central vein ICG data, such as pressure (CVP), total peripheral vascular resistance (SVR), myocardial work (RPP), heather index (HI), stroke volume index (SVI), and waveform accuracy value (WAV). Can be used to enhance the accuracy or usefulness of.

Among other things, for patients who are unable to follow more complex procedures, a simple tidal breath sample of the breath at rest is taken, which does not require coaching or compliance. Analysis of these data provides information on lung physiology and respiratory status that cannot be obtained otherwise.

Referring now to FIG. 8, there is shown an impedance plethysmograph 31 and a spirometer 32, both operatively connected to the same programmable element 33. Volume data from the spirometer is preferably sampled at the same or near the same time by impedance readings on an impedance plethysmograph. Referring now to FIG. 9, a patient is shown connected to a ventilator 34 and an impedance plethysmograph 35, both ventilator 34 and impedance plethysmograph 35 being functionally connected to a programmable element 36. The ventilator volume is sampled at the same time as the impedance plethysmograph impedance reading. Referring now to the graph of FIG. 10, for a given patient undergoing various breathing maneuvers, a graph of volume versus impedance is shown, while the data are simultaneously measured using an impedance plethysmograph and a spirometer. Was collected. The volume versus time trace represented by FIG. 11 is normal breathing. The trace represented by FIG. 12 is slow breathing and the trace represented by FIG. 13 is unstable breathing. In one embodiment, the slope of the best fit line 37 is used as the RVM calibration factor to calculate the volume from the impedance. In another embodiment, algorithms that utilize slope, shape, and/or other curve characteristics, and/or other demographic or body shape characteristics of the patient are used to calculate the calibration factor. ..

In one embodiment, simple numerical values are obtained from a ventilator or spirometer for tidal or minute ventilation for use in device calibration. One embodiment comprises a combined system in which the RVM and volumetric measurements are made simultaneously, nearly simultaneously, or sequentially by a spirometer, pneumotachometer, ventilator, or similar device. The performed and combined data is used to generate individual calibration factors for absolute volume calculation from RVM measurements for a given individual.

Example:
One calibration method is already used in small studies. Height, weight, chest circumference for maximal inspiration and normal exhalation, distance from suprasternal notch to xiphoid process, distance from mid-inferior clavicle at mid-axillary line to end of thorax, from end of thorax at mid-axillary line Distances to the iliac crest and abdominal circumference at the navel were measured and recorded. The electrodes were positioned in the posterior left-right, posterior right vertical, and anterior-to-posterior and ICG configurations discussed above. The four probes of the impedance measuring device were connected to the electrodes corresponding to one of the above configurations. The ICG position was initially connected and used only to measure the rest ICG of the subject in the supine position. The leads were then reconfigured to connect to the rear left and right positions. Once the leads were correctly positioned and the subject was in the supine position, the subject performed a breathing test, which was measured simultaneously by the impedance measuring device and the spirometer over a sampling time of approximately 30 seconds. Respiratory tests performed included normal tidal breathing (3 breaths), unstable breathing (2 breaths), slow breathing (2 breaths), forced vital capacity (FVC) (3 breaths), and maximal ventilation ( MVV) (twice). FVC and MVV were performed according to the ATS procedure. Usually, the unstable and slow tests were measured by a bell spirometer and the FVC and MVV were measured by a turbine spirometer. Preferably, the calibration can be performed all together on any type of spirometer that meets the ATS standard. Upon completion of all breath tests, the leads were repositioned in the new configuration and the tests rerun until all configurations were tested. Data was collected on a PC for impedance and turbine spirometer data and another PC for bell spirometer data. The data was then merged onto one PC and loaded into MATLAB. Preferably MATLAB or other software packages that utilize signal processing are used. Preferably, the data is loaded on a PC or other computing station. Once the data were merged, impedance and volume data from each breath test were matched together using a GUI-based program. Correlation and calibration coefficients were created for each of the test runs by comparing impedance and volume traces using MATLAB. This data was then used in Excel to predict calibration factors based on patient characteristics. Preferably, the data can be imported and analyzed into any software that has a statistical package.

Referring now to FIG. 14, a graph of BMI vs. calibration factor is shown for 7 patients. BMI is shown on the x-axis and the calibration factor is shown on the y-axis. The linear relationship between height and calibration factor in configuration D (PRR installation as described above) shows its utility in determining the calibration factor. Other physiological parameters such as height, weight, body surface area, race, gender, chest circumference, intermammary distance, age, etc. also have important relationships with the calibration factor and In form, any or all of these parameters assist in the accurate determination of the calibration factor. A combination of statistical analysis and expert system is used to determine the correlation coefficient for a given patient based on the input of said physiological parameters. Such methods can include principal component analysis, artificial neural networks, fuzzy logic, and genetic programming and pattern analysis. In the preferred embodiment, test data from a pilot study is used to train an expert system. In a preferred embodiment, existing patient demographics and lung function data are used to train the expert system. Preferably, a combination of test data from a pilot study and an existing lung function dataset is used to train the expert system.

One problem faced by some spirometers is volume drift, where a greater amount of air is inhaled than is exhaled. Additionally, long-term spirometry tests provide increased resistance to pulmonary flow, which may alter physiology and/or alter respiratory flow and/or volume. There is. These patterns can disturb the correlation coefficient for the test by changing the volume so that the volume tends to be downward with the impedance trace remaining constant. FIG. 15 presents a volume curve showing the volume drift. FIG. 16 shows the volume-to-impedance curve for that set, where volume drift is damaging the fit of the plot. In one embodiment, the device corrects this problem by subtracting the lines with constant slope values. After using this average flow method, the curves did not tend upward or downward, as seen in Figure 17, and the volume-to-impedance data fit much better, as seen in Figure 18. Volume and impedance data remain much more closely matched, giving a higher correlation and a better correlation coefficient. In one embodiment, volume drift subtraction is used in the calibration. In one embodiment, volume drift subtraction is used in deriving the calibration factor. Also by differentiating the volume curve to get the flow, subtracting the DC offset between intervals having the same lung volume at the start and end points, and then integrating to get the flow without drift artifacts. , The same utility is realized.

In another embodiment of the device, the calibration factors are based on RVM data traces and standard tables of spirometry data generated by Knudsen, Crapo, or others known to those of skill in the art. It is determined by comparing calculated values that are compared to the predicted values for the patient's tidal volume, FVC, FEV1, etc.

Data Analysis Referring now to FIG. 19, a flow chart displaying the progression of data through the analysis software is shown. Raw data is recorded by an impedance meter, digitized using an analog-to-digital converter and input to a programmable element through a standard data port. Data processing removes noise signals and motion artifacts. The analysis algorithm includes, but is not limited to, volume traces and: frequency and time domain plots of impedance and/or calculated volume traces, respiratory rate, tidal volume, and minute ventilation, without limitation. To calculate the information. In one embodiment, the analysis algorithm that transforms impedance into volume traces utilizes calibration in conjunction with spirometer or ventilator data, or in another embodiment, a physiological parameter-based calibration. Either of them. The algorithm produces a correlation coefficient that transforms the impedance scale into a volume scale when multiplied by the impedance data. In addition, the algorithm takes into account the variability of the metrics mentioned above and automatically calculates a standardized respiratory satisfaction index (RSI). This RSI contains information that integrates information from one or more measurements and/or the following measurements: respiration rate, respiration volume, respiration curve characteristics, as previously described. , Respiratory variability, or a range of acceptable values of complexity are utilized individually and in combination to provide a single number for respiratory satisfaction or unsatisfaction.

In one embodiment: , A self-learning system for patient populations is used in the calculation of RSI.

In one embodiment, the RSI is: oxygen saturation, TcpO2, TcpCO2, end tidal CO2, sublingual CO2, heart rate, cardiac output, oncotic pressure, skin water content, body water content. , And data from BMI. The advantage of this index is that it can also be understood by untrained personnel, and it can be linked to an alarm to inform a doctor or other caregiver in the event of a sudden deterioration in health. Is to be. After calculation, the processed metrics are passed to an output module, which can be embodied as a printer or displayed on a screen, or a verbal message, a visual message, or , Can be delivered by text message.

In one embodiment, the device looks at the pattern in the curve recorded during the inspiratory or expiratory phase of breathing. In one embodiment, the device looks at patterns of respiratory variability in terms of breathing number, volume, and/or location. In one embodiment, the pattern is noted with respect to the shape of the breathing curve. In one embodiment, the pattern analysis includes values derived from the inspiration slope. In one embodiment, the pattern analysis includes values derived from the exhalation slope. In one embodiment, the pattern analysis includes a combination of parameters, where the parameters are: respiration rate, minute ventilation, tidal volume, inspiratory slope, expiratory slope, respiratory variability. Any or all may be included. In one embodiment, these parameters are used in the calculation of Respiratory Health Index (RHI), which provides a standardized, quantitative measure of ventilation adequacy. In one embodiment, the RHI is coupled with an alarm that alerts the patient when the breath falls below what is considered to be appropriate, or to the extent appropriate. If you experience any abrupt changes, make a sound. In one embodiment, the device provides information to calculate the RHI. Preferably, the device calculates and displays the RHI. In one embodiment, the respiratory health index is compared against a universal calibration based on patient characteristics. In one embodiment, RHI provides quantitative data to a calibrated system for a particular patient.

Referring now to FIG. 27, the time delay or phase lag of the impedance and volume signals is shown. In this particular figure, the delay was found to be 0.012 seconds. The phase lag between volume and impedance signal is an important issue addressed in one embodiment. Due to the elastic and capacitive nature of the tissue of the pleura and lungs, which creates a slight delay between the moving diaphragm and the air flowing through the lungs, impedance and volume signals There is a time lag between. In one embodiment, this phase difference is used as a measure of lung stiffness and airway resistance. Frequency phase analysis allows the user to find the phase angle. Larger phase offsets indicate a higher degree of airway resistance to movement. Phase angle calculation is accomplished by comparing simultaneously recorded and synchronized RVM curves to flow, volume, or pressure curves recorded by a spirometer, pneumotachometer, ventilator, or similar device. .. In one embodiment, the phase lag between volume and impedance signal is a component of the algorithm used to calibrate the system to a given individual. In one embodiment, the phase lag is used to calibrate the system for universal calibration. When using external pressure, flow, or volumetric devices to calculate the calibration factor, the leading curve is shifted by the magnitude of the phase lag to correlate in time with the trailing curve. .. This embodiment improves the accuracy of the calibration algorithm. Virtual phase lag calculated based on patient characteristics, including demographic information, physiological measurements, and lung function test metrics when external pressure, flow, or volumetric devices are not used for calibration To be done.

In one embodiment, the phase lag is corrected by the RVM algorithm in matching both impedance and volume. In one embodiment, the phase lag data is presented independently as a standardized index to establish lung compliance and stiffness measures. In one embodiment, phase lag data is integrated into the respiratory health index as a measure of respiratory status.

In one embodiment, frequency domain analysis is applied to RVM measurements. Preferably, at least one frequency domain plot, such as a Fourier transform, is displayed to the operator. Preferably, at least one two-dimensional frequency domain image of the RVM data, such as a spectral graph, is displayed to the operator, where one dimension is frequency and the other dimension is time, and The size of the signal at the location is represented by color. Preferably, the frequency domain information is used to assess respiratory health or lesions. Preferably, the alarm will alert a medical professional if the frequency domain data indicates a sharp deterioration in patient health.

In the preferred embodiment, RVM measurements are used as the basis for complexity analysis. In one embodiment, the complexity analysis is performed on the RVM signal only. Preferably, the RVM measurement is used in combination with other physiological measurements such as heart rate, urine output, EKG signal, impedance electrocardiogram, EEG, or other brain monitoring signals.

In a preferred embodiment, the RVM measurements are: patient generated respiratory pressure ventilator measurement, patient generated respiratory flow ventilator measurement, patient generated respiratory volume ventilator measurement, ventilator generated breathing. Treat or monitor a patient, including ventilator measurements of pressure, ventilator-generated respiratory flow ventilator measurements, ventilator-generated respiratory volume ventilator measurements, infusion pumps, or other devices used to treat patients Utilized as a component of complexity analysis in combination with the data provided by the device used to perform, RVM measurements can be used to quantify breath-to-breath variability. One embodiment of the device is used to define specific points along a breathing curve, thereby determining breath-to-breath variability in respiratory rate, such as inspiratory peak or expiratory nadir. calculate. Preferably, the peak or nadir of each breath is automatically identified. In one embodiment, the device provides data with accounting for breath-to-breath variability in inspired volume. In one embodiment, the device provides data describing breath-to-breath variability or slope complexity, or other characteristics of respiratory volume or flow curves. In one embodiment, the device provides data and, with that data, collecting data from different locations on the body using pairing of the same or different electrodes, chest or abdomen, or one or the other. Compute the variability or complexity associated with the location of respiratory effort, such as one half-thoracic versus the other half-thorax. Preferably, the device calculates breath-to-breath variability or complexity of one or more of these parameters. Preferably, the device presents the variability or complexity analysis in a form that is easy for the user to interpret. In one embodiment, the device comprises two or more of variability or complexity among: respiratory rate, respiratory volume, location of respiratory effort, slope, or other characteristic of respiratory volume or flow curve. Combines data from sources to provide an advanced assessment of respiratory function. In one embodiment, the device analyzes variability or complexity data intermittently or continuously and presents the data at intervals such as every 10 minutes, every 30 minutes, or every hour. .. Preferably, the device presents variability analysis in less than 10 minutes, less than 5 minutes, less than 1 minute, or near real time. In one embodiment, any variability or complexity of respiratory parameters may be quantified by linear or non-linear analytical methods. Preferably, any variability or complexity of respiratory parameters can be quantified by non-linear kinetic analysis. In one embodiment, approximate entropy is used by the device for data analysis. In one embodiment, analysis of data variability or complexity is combined with volumetric data to provide a combined index of respiratory function. In one embodiment, the variability or complexity analysis data is combined with other parameters and presented as a respiratory wellness index or respiratory health index.

In a preferred embodiment, RVM measurements, or complexity analysis of RVM signals, are utilized as at least some of the information used in goal-directed therapy. In a preferred embodiment, the RVM measurements or complexity analysis of the RVM signal provide information for decision support. In a preferred embodiment, RVM measurements, or complexity analysis of RVM signals, are utilized as at least a portion of the patient data required for a controlled loop system.

Uses in Imaging In one embodiment of the device, respiratory cycles are measured by one or more methods including, but not limited to, impedance pneumography, end tidal CO ₂ , or pulse oximetry, while the heart is It is imaged or otherwise measured using echocardiography, which may be embodied as 2D echo, 3D echo, or any other type of echocardiography. Time series data from the echocardiogram is marked as having a particular accuracy rating based on the respiratory movements recorded by the respiratory monitor. In one embodiment, echocardiographic data below the accuracy threshold is discarded. In another embodiment, echocardiographic data is weighted based on its accuracy rating and the least accurate data is least weighted. The device generates a composite image or video of the heart and movement of the heart based on the most accurate echocardiographic data. In one embodiment, echocardiographic data is recorded over two or more cardiac cycles, and then, after analysis and accuracy rating, the best data is to produce a composite image of the heart or a video of the cardiac cycle. Used for.

Other embodiments include combining respiratory cycle measurement and quantification with other cardiac imaging techniques for the purpose of improving accuracy. Methods of cardiac imaging can include Doppler flow measurements, radionuclide tests, synchronized CT, and synchronized MRI. Other embodiments include chest cycle, abdomen, including respiratory cycle measurements by RVM and diagnostic CT or MRI, catheter-based therapy, direct cardiac ablation, radiofrequency ablation of tumors, radiotherapy of tumors, and Combining with other diagnostic or therapeutic modalities of other body parts. In a preferred embodiment, RVM and cardiac impedance data are utilized together for diagnostic imaging or anatomically direct therapy data collection or data analysis timing.

In another embodiment of the device, respiratory impedance measurements or data from a complexity analysis of RVM measurements are used to generate images of the lungs. In another embodiment of the device, data from a complexity analysis of RVM measurements and cardiac impedance measurements are used to generate images of the heart and lungs. In the preferred embodiment, the heart and lungs are imaged simultaneously. In one embodiment, the device is used to generate a 2D image, video, or model of the heart and/or lungs. In a preferred embodiment, the device produces a 3D image, video, or model of the heart and/or lungs.

Detecting Lesions and Improving Monitoring In one embodiment, the device provides RVM data, the RVM data being associated with or with variability or complexity analysis. Used to assist decision making, such as extubation or intubation for mechanical ventilation. In one embodiment, the device provides RVM data, the RVM data relating to drug administration or other medical intervention with or without variability or complexity analysis. Aid decision making. In one embodiment, the device uses variability or complexity information alone or in conjunction with volumetric data as part of an open or closed loop control system for adjusting ventilator settings. In one embodiment, the device provides variability or complexity information, either alone or as volume data or RVM provided as part of an open or closed loop control system for adjusting drug dose. Use with other analysis of curves. This embodiment is useful for preterm infants to optimize the management of the pressure ventilator and also for patients with endotracheal tubes without cuffs. In one embodiment, the device uses the variability or complexity information as part of a patient management system, either alone or in conjunction with volume data or other analysis of the respiratory curve provided by the RVM for patient management. The system monitors patient status, recommends drug delivery, and then reassess the patient to direct further behavior.

In one embodiment, the device uses variability or complexity analysis of the RVM signal alone, volume data alone, curve analysis alone, or any combination thereof. And trigger an alarm indicating a change in patient condition. In another embodiment, symbol distribution entropy and bit-perword entropy are used to measure the probability of patterns in the time series. In another embodiment, a distribution similarity methodology is used. In one embodiment, when the device detects a change in respiratory complexity or respiratory complexity below a certain threshold, or a more unnatural breathing pattern associated with a lung lesion or disease state. When it detects, the device sounds an alarm. In one embodiment, the device sounds an alarm when the device detects a change in the combined measurement of respiration and heart rate complexity above a certain threshold.

In one embodiment, the RVM measurements are integrated into an open or closed feedback loop to monitor the ventilation for respiratory arrest warning signs, thereby ensuring safe dosing of the drug, thereby ensuring proper ventilation. Report sex. In the preferred embodiment, the RVM is integrated into a system with a ventilator that provides an open or closed feedback loop, and ventilator adjustment is provided by the open or closed feedback loop. The difference between the RVM measurement and the volume or flow measurement generated by the ventilator or spirometer can be used to provide information for diagnostic and therapeutic guidance. By using RVM monitoring, with or without additional information from end-tidal CO ₂ or pulse oximetry measurements, this embodiment gradually reduces ventilator support. By automatically observing the RVM and other parameters, the patient is weaned, alerting the doctor that extubation is ready or not being able to proceed. To alert. The system can additionally include machine intelligence in the form of supervised and unsupervised learning based on patient-specific and/or population-based data. Preferably, the system can provide clinical guidance and suggestions for ventilator adjusted use.

This combined system with either pulse oximetry or ETCO2 or both provides an open or closed loop system for delivering anesthetics or other respiratory depressant drugs such as benzodiazepines or propofol. Can be used as.

In one embodiment, an analysis algorithm detects the presence of specific breathing patterns maintained in the expert system database and informs the physician or other health care provider of potential lesions of interest. In one embodiment, the breathing pattern for a given lesion is recognized and, in a preferred embodiment, quantified. In another embodiment, the lesion is located.

In a preferred embodiment, the device recognizes specific patterns associated with respiratory volume, curves, variability or complexity, or other analysis of RVM data.

In one embodiment, the device recognizes patterns associated with impending respiratory failure or respiratory arrest and delivers audible and/or visual alerts or warnings. In one embodiment, the device analyzes respiratory data or trends in the data and makes intubation and mechanical ventilation recommendations. In one embodiment, the device analyzes breathing pattern data and adjusts the level of infusion of an anesthetic or other respiratory depressant drug, such as propofol.

In one embodiment, the device is associated with a particular disease entity or pathology, such as congestive heart failure, asthma, COPD, anesthetic-induced respiratory depression, or impending respiratory failure. Recognize patterns. In one embodiment, the device alerts the physician of this lesion. In one embodiment, the device quantifies the extent of lesions. In one embodiment, the device recognizes patterns of congestive heart failure and provides data on trends towards improvement or worsening over time or by associated medical intervention.

Preferably, the impedance measurement element of the device is capable of producing impedance cardiograph (ICG) measurements. Preferably, the device detects impedance variability associated with heart rate variability. Preferably, the device detects impedance variability associated with the variability of the respiratory waveform or other respiratory parameter and utilizes the variability of heart rate and respiratory rate, volume, or waveform to detect cardiac, respiratory, And predict lung complications. Preferably, the device maintains an alarm regarding unsafe lung variability or complexity or predetermined limits associated with combined heart rate and respiratory variability or complexity.

In another embodiment, end-tidal CO ₂ (ETCO ₂ ) is used in addition to, or instead of, a subjective assessment to determine the RVM baseline. In one embodiment, RVM is coupled with ETCO ₂ measurements to provide additional information about respiratory status.

In another embodiment, the RVM is coupled with pulse oximetry to provide information on both ventilation/respiration and oxygenation. More complex RVM systems _couple standard RVM measurements with either ETCO ₂ or pulse oximetry, or both. This combined device provides additional information about breathing for sedated patients and enhances patient monitoring.

In a preferred embodiment, lung volume and minute ventilation measurements are used to assess patient suitability after extubation in a quantitative manner. Minute ventilation is specifically used for patients undergoing surgery. Preferably, preoperative measurements of tidal volume or minute ventilation are taken as a baseline for a particular patient. Preferably, the baseline is used postoperatively as a comparison between preoperative and postoperative respiratory conditions. Postoperative recovery of tidal or minute ventilation trends during surgery or procedure, or in post-anesthesia recovery room, in the intensive care unit, or on the hospital floor Used to monitor patients during the period. This trend can provide an accurate measure of differences and changes in patient respiration from pre-treatment baseline and indicate when the patient returns to baseline levels of respiration. In a preferred embodiment, the device directly assists the physician in making an appropriate extubation decision by defining the appropriate level of breathing specific to that patient. In one embodiment, absolute lung volume is compared to pre-calibrated data derived from patient characteristics to determine the presence of restrictive and/or obstructive lung disease and other respiratory conditions. Used for. Absolute volumetric data may be particularly useful in PACUs and ICUs as a complement to existing quantitative data.

The system preferably monitors the patient's respiratory status before and after extubation, provides recommendations for additional respiratory treatments or medications (if needed), or for further treatment. Can be shown to be absent, and the patient is ready for transfer from mechanical ventilation, CPAP, BiPAP, or high flow 02. The system is preferably capable of detecting small breaths that may otherwise not be detected by the ventilator. Preferably, the system may be capable of providing extubation trials prior to actual extubation while monitoring data to support extubation.

In one embodiment, the system preferably provides an indication of the need to intubate or re-intubate the patient. The indication can be auditory and/or visual. In another embodiment, the system controls external ventilation and respiratory therapy or therapy, preferably via either open and closed loops, based on RVM measurements.

In another embodiment, the system preferably performs a real-time analysis of expiratory and inspiratory impedance or the shape of the tidal volume signal curve to prepare for extubation, the need for intubation, the need for reintubation. At least one of: the need and the need for additional treatment:. In another embodiment, the system preferably provides real-time feedback and control of the ventilator from alveolar hyperinflation resulting from either mechanical ventilation (VILI) or natural ventilation (SILI). Prevent damage to the lungs or through excessive driving pressure.

In another embodiment, the system preferably performs a real-time analysis of flow volume loops, in particular hysteresis in those loops, and is ready for extubation, the need for intubation, At least one of: need for re-intubation, need for additional treatment will be determined. In another embodiment, the system preferably provides real-time feedback to the lungs from alveolar hyperinflation resulting from either mechanical ventilation (VILI) or natural ventilation (SILI). It will prevent damage or prevent damage through excessive drive pressure. In another embodiment, the system preferably provides real-time feedback identifying atelectasis, lung collapse, or closure, in which case the alveoli have little or no volume.

Use in PCA Feedback and Drug Dosing Optimization One use of the device is to use cardiac and/or respiratory data measured and recorded by one, some, or a combination of the techniques listed herein. Used to determine the impact of one or more drugs or other medical interventions on a patient. In certain embodiments, respiratory monitors are used to determine the side effects of analgesics on the body and prevent or assist in the prevention of respiratory failure or other disorders resulting from adverse reactions or overdose. It

In a preferred embodiment, the device is paired with or integrated into a patient managed analgesic (PCA) system. This can be accomplished electronically through communication between the device of the invention and an electronic PCA system, or by an integrated monitor/PCA system, or a patient-administered PCA. Achieved by setting in the monitor showing. In this embodiment, administration of analgesia or anesthesia is limited based on the risk of respiratory or other complications predicted by the device. If the PCA system is not electronic, or if the analgesic is delivered by the human hand, the device is at an increased risk of respiratory complications and the dosage should be reduced. Make recommendations.

Another embodiment of the device of the present invention is a diagnostic/treatment platform. Monitoring devices include: Medication regimens, therapeutic regimens, use of inhalers, use of nebulizers, use of drugs targeting the respiratory system, use of drugs targeting the cardiovascular system, targeting asthma. Paired with one or more of drug use, COPD, CHF, cystic fibrosis, bronchopulmonary dysplasia, pulmonary hypertension, and drug use targeting other diseases of the lung. This embodiment of the device determines the effectiveness of possible medical and non-medical interventions on respiratory status or respiratory health, suggests a change in regimen for optimization, and/or patient complications. It is used to suggest appropriate intervention when at risk of disease.

In one embodiment, the RVM is a behavioral algorithm or an algorithm that includes information about any of the following patient medical conditions, environmental factors, and demographic group or patient-wide behavioral factors: Be paired. In the preferred embodiment, one of the algorithms described above may indicate the need to obtain RVM measurements. More preferably, RVM measurements are used in conjunction with behavioral/medical/environmental algorithm data to provide information to indicate an action or therapy. Examples of the use of this embodiment of the device include a patient's previous respiratory complications or chronic respiratory illness, and/or allergies, along with behavioral events known to exacerbate said condition, as inputs. It will be an algorithm. By including information from the patient's schedule (eg, attending an outdoor event during an allergy season or participating in a sports competition), the system recommends that the patient make an RVM measurement, and then Make recommendations on whether to maintain or increase your usual dose of the drug. The software can also encourage the patient to take the drug to the event, and can generally remind the patient to take their drug. Another example may be where the patient has an asthma attack or other respiratory complications. RVM data includes minute ventilation, tidal volume, time (ie, ratio) for inspiration to expiration, shape of the respiratory curve during normal breathing, during the deepest possible breathing or other breathing maneuvers. Any of the measured parameters, including the shape of the breathing curve, can be used to assess the severity of this attack. The data can then be independently prompted or can be used in conjunction with other information, such as: do nothing, rest, use inhaler, take medication, nebulizer Make a decision for the patient to perform an action including one of: Information about required actions can be used to identify specific patients or groups of patients with similar illnesses, demographically similar patients, specific medical, anatomical or behavioral profiles. It can be part of a behavioral or other algorithm that is designed for the patient to have, or for the general patient. Preferably, after action, the patient is instructed to repeat the RVM measurement in order to assess therapy suitability. Preferably, repeated measurements of the patient are compared to measurements prior to therapy or other intervention to note changes. Additional information from this comparison, or the data itself taken after the therapy, is used alone or in combination with other patient data to make recommendations regarding further medical decisions or actions.

For example, an asthma patient has symptoms and decides to obtain an RVM measurement, or is instructed by a disease management algorithm to obtain an RVM measurement. RVM data is analyzed by the device, utilized independently, or compared to historical baseline or last taken measurements. Based on these, with or without other patient-specific inputs, such as heart rate, etc., the device recommends that the patient use an inhaler. Then a second set of RVM data is taken. The RVM data is compared to previous RVM data taken prior to treatment. The device then follows the decision tree that the patient is improving and does not require further therapy, the patient needs to repeat the dosage, the patient needs to call a doctor, or the patient immediately Tell the patient that you need to go to the hospital. In a preferred embodiment, the RVM data is combined with behavioral algorithms developed demographically or for a particular patient to optimize the patient's recommendations.

Use in PACU/ICU In one embodiment, the device is a post-surgical anesthesia recovery unit (PACU), either as a stand-alone monitor or as an adjunct to an existing monitor or incorporated into an existing monitor. ) Used in the setting. Within the PACU, RVM volume is calculated and compared to pre-calibrated data derived taking into account BMI, height, weight, chest circumference, and other parameters. The device is used to supplement the existing quantitative data supporting decision making within the PACU. In one embodiment, in the operating room, RVM data is correlated with end-tidal carbon dioxide measurements to provide a more comprehensive assessment of respiratory status. RVM-derived measurements, including minute ventilation, are used to compare patient status before, during, and after surgery or procedures, and anesthesia/anesthetic-induced respiration. Used to record the effects of suppression. RVM is used to support more subjective assessments made by clinicians in the PACU by providing quantitative justification for particular decisions, including reintubation decisions. The device also provides a subjective assessment of patients on the hospital floor as a monitor for decreased respiratory status and as an alarm about the need to re-intubate or perform another intervention to improve respiratory status. to support. Preferably, the RVM measurements will aid in the conditioning of anesthetic analgesics, sedatives such as benzodiazepines, or other drugs that have a respiratory depressant effect. In one embodiment, the above-described use of RVM in a PACU setting provides ICUs such as neonatal ICU, surgical ICU, medical ICU, pulmonary ICU, cardiac ICU, coronary care unit, pediatric ICU, and neurosurgery ICU. It is realized in the setting. In another embodiment, the RVM device is used in a step-down unit or standard hospital bed setting to track respiratory conditions.

Late in the post-operative period or otherwise, respiratory pattern measurements, including tidal volume, respiratory rate, minute ventilation, inter-breath interval or volume variability, or RVM signal complexity Can be compared to the baseline value measured before. This can directly aid in extubation decisions by defining what is the appropriate level of breathing that is specific to that patient. In another embodiment of the device, RVM monitoring identifies problems commonly associated with ventilators, such as poor endotracheal tube positioning, hyperventilation, hypoventilation, rebreathing, and air leaks. The system also identifies air leaks through the chest tube or uncuffed tube. Air leaks will cause a downward trend to appear on any direct volume measurement, but this trend does not exist on the impedance trace, so the device measures the volume or flow directly. It is possible to detect and report an air leak in the device. In a preferred embodiment, the system may include hemithoracic-specific abnormalities such as those associated with, for example, the following lesions: pneumothorax, pulmonary contusion, rib fracture, hemothorax, chylothorax, hydrothorax, and pneumonia. Identify trends.

In one embodiment, the device is used during supervised anesthesia management (MAC) to monitor respiratory status, aid drug and fluid administration, and imminent or pre-existing respiratory or respiratory failure indications. And, if necessary, assist in the decision to be intubated.

In another embodiment of the device, RVM monitoring identifies problems commonly associated with ventilators such as poor endotracheal tube positioning, hyperventilation, hypoventilation, rebreathing, and air leaks. In one embodiment, RVM measurements are combined with ventilator-derived data to provide additional data regarding physiology. An example of this is that differences are recorded by comparing RVM measurements to inspired or exhaled flow or volume measured on a ventilator to assess "work of breathing" in a quantitative manner. Is to get.

In another embodiment, RVM measurements are taken after surgery in patients still under the influence of anesthesia or analgesics to monitor patient recovery. Recording a baseline tidal volume curve for the patient during normal pre-operative conditions provides a comparative baseline for monitoring during and after surgery. Returning to a similar tidal volume curve is one signal for respiration recovery after ventilator removal. In this embodiment of the invention, the device is used to assess the success of extubation and determine if re-intubation is necessary. The invention described herein is non-invasive and without intervening in a stream of inhaled/expired air, or impeding airway flow or altering airway circuits. Allows these measurements to be taken without contamination.

In one embodiment, the device is specifically directed to patients undergoing supervised anesthesia management, including orthopedic procedures, cataract surgery, and patients undergoing endoscopic examination of the upper and lower GI tract. It is used in the Outpatient Surgery Center.

Use in Diagnostics In one embodiment, the device is used to quantify respiratory parameters during performance-based testing. In a preferred embodiment, the device is used to quantify respiratory parameters in tests of cardiovascular function, including stress tests. In a preferred embodiment, the device is used in combination with one of the following tests to assess the effect of the test on breathing. In a preferred embodiment, the device reports the effects of certain drugs, such as exercise or dopamine, on the body's overall physiology or metabolism, although these effects have been described elsewhere. Reflected by changes in respiratory volume, respiratory pattern, respiratory rate, or a combination thereof, including advanced analysis of breath-to-breath variability/complexity, fractal or entropy-based analysis. In a preferred embodiment, the device is used to assess the safety of a given level of exercise or pharmacological stress.

In a preferred embodiment, the variability or complexity analysis of RVM measurements is performed in concert with standard lung function tests. In a preferred embodiment, variability or complexity analysis of RVM measurements is performed with standard cardiovascular physiology tests such as stress tests, gait tests for lameness, or other performance-based tests. Cooperatively with or without heart rate variability/complexity analysis.

In a preferred embodiment, the device is used for assessing the effects of a drug on the respiratory system, including bronchodilators for diagnostic purposes, therapy monitoring, and optimization including effects on both the heart and lungs. To be done. More preferably, the above device combines the respiratory information obtained by impedance or other described methods with EKG information for EKG evidence of heart rate, heart rate variability, ischemia or arrhythmia. In a preferred embodiment, the device is used to assess the effects of bronchoconstrictor, such as in provocation tests. In various embodiments, the device obtains continuous or intermittent RVM measurements. In a preferred embodiment, the device provides trends in RVM data.

In a preferred embodiment, the device is used to assess the effects of metabolic stimulants or cardiovascular drugs, including beta blockers, alpha adrenergic agonists or blockers, beta adrenergic agonists or blockers. It In a preferred embodiment, the device is used during a stress test to establish the level of effort made, or to establish an unsafe condition for the pulmonary system and to terminate or modify the test. To be done. The stress introduced into a patient is generated by a variety of means, including but not limited to exercise and/or drug delivery. In a preferred embodiment, the device exhibits or cooperates with other techniques previously described to provide an overall level of exercise. In a preferred embodiment, the device is used as a stand-alone device for measuring the effects of exercise or other stimulants on the lung system.

In another embodiment of the device, respiratory information is combined with cardiac information to define the level of motion associated with EKG changes associated with heart disease. In another embodiment of the device, the system combines respiratory information with cardiac information to determine an athlete's level of exercise.

In another embodiment, the device is for use in a home, playground, military environment, or out-of-hospital setting with or without pairing respiratory signals with cardiac impedance or EKG measurements. Provides warning of the potential adverse effects of exercise levels on health or heart condition. One embodiment of the device is a Holter monitor, which includes: respiratory effort, level of activity, physiological status, associated with different rhythms, depolarizations, or other cardiac pathophysiology. Alternatively, it outputs a value for one or more of the metabolism.

One embodiment of the present invention is similar to a Holter monitor that monitors one or more physiological parameters over hours to days in a hospital, home, or other setting. One embodiment of the device is combined with a Holter monitor or a critical care monitor that monitors decompensation effects specifically related to heart failure. A similar embodiment of the device monitors and outputs "lung water" measurements. In one embodiment, the device is included in a disease management system for congestive heart failure.

In the most preferred embodiment, the device provides a continuous measurement, which can be run for an extended period of time and also demonstrates the efficacy of exercise or diagnostic drugs, therapeutic monitoring or drug development. It is possible to deliver a time curve.

One embodiment of the device provides trend data over minutes, hours, and days for patients with various disease states, including various disease states such as chronic obstructive pulmonary disease, congestive heart failure, pulmonary hypertension. , Pulmonary fibrosis, cystic fibrosis, interstitial lung disease, restrictive lung disease, mesothelioma, after thoracic surgery, after cardiac surgery, after thoracotomy, after thoracotomy, after rib fracture, Includes post-pulmonary contusion, post-pulmonary embolization, cardiac ischemia, cardiomyopathy, ischemic cardiomyopathy, restrictive cardiomyopathy, dilated cardiomyopathy, infectious cardiomyopathy, and hypertrophic cardiomyopathy. Preferably, the device provides information about changes in breathing in these disease states associated with intervention or provocation test procedures.

In one embodiment of the device of the present invention, the system is used to diagnose various diseases. In a preferred embodiment, the device is used to assess the risk of developing pneumonia. In another embodiment, the device is used to assess the risk that pneumonia therapy is ineffective and suggests corrective action. Another embodiment of the invention is used for the assessment of functional deterioration or recovery associated with a disease, where the disease is due to: pneumonia, heart failure, cystic fibrosis, interstitial fibrosis, water level, heart failure. Including, without limitation, hemostasis, pulmonary edema, blood loss, hematomas, hemangiomas, fluid accumulation in the body, bleeding, or other disorders. This information can be used for diagnosis as described above, or can be measured by the device or input into the device to provide a comprehensive respiratory satisfaction index (cRSI). It can be integrated with volumetric measurements or other physiological measurements.

In one embodiment, to collect disease-specific information, to use disease-specific algorithms, and to deliver either optimized respiratory volume data or respiratory diagnostic data related to the specific disease. At the same time, disease specific modules can be generated.

In a preferred embodiment of the invention, respiratory curve analysis is used to diagnose medical conditions. In one embodiment, the system utilizes a provocation test to: Tidal volume, residual volume, pre-expiratory volume, pre-inspiratory volume, maximum inspiratory volume, inspiratory capacity, vital capacity, functional residual capacity. A measurement or estimate of one or more of volume, residual capacity, forced vital capacity, forced expiratory volume, forced expiratory flow, forced inspiratory flow, peak expiratory flow, and maximal forced ventilation is determined. In this embodiment, diagnostic tools, such as flow volume loops, are generated by software running on the system for the diagnosis of various cardiopulmonary or other diseases.

Respiratory curve analysis can also be used to assess cardiopulmonary or other diseases without provocation tests. In one embodiment, the algorithm monitors TV, MV, and RR trends to provide a respiratory satisfaction metric or respiratory satisfaction index (RSI). In another embodiment, the algorithm analyzes individual breaths as input to diagnose respiratory conditions. In this embodiment, one or more of the following parameters are calculated at the base of each breathing: inspiration time (I _t), expiratory time (E _t), I _t: ratio of E _t, percent inspiration time , Tidal impedance, tidal volume, and area under the curve. In this embodiment, various parameters are output through the user interface of the system or through a printable report for the user to assess respiratory disease status. In the preferred embodiment, the algorithm analyzes parameters for acting as a diagnostic aid. In this embodiment, the system outputs an index of disease severity, or positive/negative readings for disease.

In one embodiment, the device is implanted. In the preferred embodiment, the device is powered from a battery such as a pacemaker. In one embodiment, the device is combined with a pacemaker or defibrillator. In one embodiment, the device is adjusted, calibrated, or interrogated using external components.

FIG. 40 illustrates an embodiment of the present invention in which the impedance measurement device is in data communication with a high frequency chest wall vibrometry (“HFCWO”) vest. It has recently been observed that during best oscillating therapy, the patient's minute ventilation is reduced by up to 50%. Improved efficiency can provide significant health benefits to patients who have the difficulty of providing oxygenation of the bloodstream during breathing. In a preferred embodiment, the HFCWO vest automatically provides therapeutic levels (frequency, intensity, length) developed to optimize O2 to CO2 transfer in the lungs. The goal is to optimize the transfer of oxygen and CO2 by using HFCWO vest. By increasing turbulence in the lungs during inspiration and exhalation, better oxygen and CO2 transfer can be achieved. Preferably, reducing the work of breathing reduces the likelihood of respiratory failure. In addition, patients undergoing oxygen therapy combine oxygen therapy with HFCWO best therapy to maximize oxygenation, improve CO2 removal, and reduce work of breathing, thereby improving lifespan. It can be extended.

Typically, HFCWO best therapy provides a 10 minute treatment to eliminate exudates. The use of this product preferably allows for better oxygenation. Product use can be continuous for up to 24 hours/day. The system may be customized to activate when the patient needs additional oxygenation efficiency during active hours such as walking. In contrast to exudate removal, the parameters of oscillation can be optimized to maximize oxygen transfer in the lung while minimizing patient discomfort.

As shown in FIG. 40, the sensor for obtaining a physiological bioelectrical impedance signal from the patient is preferably functionally connected to the computing device. The computing device preferably analyzes the physiological bioelectrical impedance signal and provides an assessment of the minute ventilation and tidal volume of the patient based on the analyzed bioelectrical impedance signal. The computing device also preferably monitors the signal over time and provides the signal to the HFCWO vest.

Preferably, the HFCWO vest is based on levels of physiological parameters, such as tidal volume, minute ventilation, and respiration rate during therapy, as determined by the computing device. , Strength, length) are automatically adjusted. In addition, general session-by-session lung performance can be tracked to verify the efficacy of therapy and the need to expand or modify therapy levels (TV, RR, MV). The goal is to optimize oxygen and CO2 transfer and increase lung turbulence during inspiration and exhalation through the use of HFCWO vest.

In addition, the shape of the bioimpedance exhalation/suction curve can be an indicator of successful therapy. Appropriate curves for maximizing oxygen transfer can be identified and the level of HFCWO vest (frequency, intensity, length of therapy, baseline compression) can be determined by the desired respiratory curve and the required oxygenation and/or CO2. It can be adjusted to obtain extraction as well as to minimize work of breathing.

Additionally, a pulse oximeter can be added to the system as an indicator of enhanced compression therapy and improved oxygenation success. The level of therapy can be optimized by monitoring the oxygenation response over time. CO2 monitoring can be added to the system with either end-tidal or transcutaneous CO2 monitoring. In addition, patients receiving oxygen therapy should combine oxygen therapy with HFCWO Best therapy to preferably maximize oxygenation, improve CO2 removal, reduce work of breathing, and thereby prolong life. Is possible.

FIG. 41 illustrates an embodiment of the present invention in which the impedance measurement device is in data communication with a mechanical ventilation therapy device. The mechanical ventilation therapy device can be a CHFO system, ventilator, CPAP, BiPAP, CPEP (continuous positive expiratory pressure), high flow O ₂ device, or another non-invasive ventilation device. Preferably, the system includes a sensor for obtaining a physiological bioelectrical impedance signal from the patient and is operably connected to the computing device. The computing device preferably analyzes the physiological bioelectrical impedance signal and outputs a minute ventilation and tidal volume assessment of the patient based on the analyzed bioelectrical impedance signal. The system can also monitor the signal over time and provide the signal to a mechanical ventilation device. The mechanical ventilation device preferably causes a better oxygenation efficiency in the lungs. The mechanical ventilation device is preferably capable of adjusting the frequency, intensity, and/or baseline suction and exhalation pressures of the vibration.

The bioelectrical feedback signal provides an indication for successful oxygenation. Characteristic values for tidal volume, minute ventilation, and respiratory rate will change. By monitoring the changes, the system can automatically adjust the parameters of the mechanical ventilation device to optimize physiological response and system efficiency. Additionally, a pulse oximeter can be added to the system as an indicator of successful mechanical ventilation therapy. Improved oxygenation and CO2 transfer may preferably be achieved, or reduced work of breathing may preferably be achieved to reduce the likelihood of respiratory failure. The level of therapy can be further optimized by monitoring the oxygenation response over time. In addition, the overall length of the therapy can be adjusted. General session-by-session lung performance can be tracked to verify the effectiveness of ventilation and the need to dilate or modify therapy levels (TV, RR, MV).

In addition, the characteristic shape of the bioimpedance suction and exhalation curves is an indicator of successful therapy. By adapting the therapy to obtain the desired excretion curve, the system is able to optimize oxygenation efficiency. Appropriate curves for maximizing ventilation can be determined and the ventilator's adjustment level (frequency, intensity, therapy length, baseline pressure) can be adjusted to obtain the desired respiratory curve. In addition, patients receiving oxygen therapy can combine oxygen therapy with mechanical ventilation to maximize oxygenation and prolong life. Additionally, the level of compliance with using the system and getting sufficient therapy can be monitored by analyzing the volume of air entering and leaving the lungs.

By using tidal volume, MV, and RR, the relative success of widening the airways can be determined.

Mechanical ventilation therapy can be combined with aerosol delivery to provide an additional therapeutic regimen. Since the inhalation of the aerosol will essentially modify the impedance characteristics of the lungs, the level of respiration and the effect of these two combined treatments can also be optimized. For example, during treatment, tidal volume and characteristic inhalation and expulsion curves are monitored before, during, and after treatment, with appropriate optimization of positive pulmonary and airway expiratory pressure during inflation, or , It is possible to guarantee a properly cleared lung.

FIG. 42 illustrates an embodiment of the invention in which the impedance measurement device is in data communication with an oxygenation therapy device. The system preferably includes a sensor for obtaining a physiological bioelectrical impedance signal from the patient and is operably connected to the computing device. The computing device preferably analyzes the physiological bioelectrical impedance signal and provides an assessment of the minute ventilation and tidal volume of the patient based on the analyzed bioelectrical impedance signal. The computing device additionally preferably monitors the signal over time and provides the signal to the oxygen therapy system. Preferably, oxygen therapy provides oxygen via a mask or nose cannula. The bioelectrical feedback signal provides an indication of the success of the level of airway dilation. The characteristic shape of the bioimpedance expansion curve is an indicator of the entry of air into the lungs.

By combining inspiration and exhalation pressure monitoring with impedance signals, the oxygenation therapy system can synchronize delivery of oxygen in the cannula to ensure optimal oxygen uptake through the nose cannula.

For oxygen therapy using a mask, the feedback mechanism of oxygen delivery can be optimized as well. In addition, by using both the impedance signal and the mask pressure, the oxygen system can determine how well the mask is applied to the patient and how well the circuit is maintained (no kinks and Leak-free) can be determined more reliably.

FIG. 43 illustrates an embodiment of the invention in which the impedance measurement device is in data communication with an aspiration therapy device. The system preferably includes a sensor for obtaining a physiological bioelectrical impedance signal from the patient and is operably connected to the computing device. The computing device preferably analyzes the physiological bioelectrical impedance signal and provides an output of the patient's minute ventilation and tidal volume assessment based on the analyzed bioelectrical impedance signal. The computing device also preferably monitors the signal over time and provides the signal to the aspiration therapy device.

Aspiration therapy preferably causes mobilization of fluids in the lungs. Aspiration therapy can be adjusted for frequency and intensity of vibration. Also, baseline inspiration and exhalation pressures can be adjusted and the overall length of therapy can be adjusted.

The bioelectrical feedback signal preferably provides an indication of successful mobilization of the secretion. As aspiration draws fluid, characteristic values for tidal volume, minute ventilation, and respiratory rate will change. By monitoring the changes, the system is preferably able to automatically adjust the aspiration parameters to optimize the physiological response.

In addition to the characteristic shape of bioimpedance, the elimination curve is an indicator of successful therapy. By adapting the therapy to obtain the desired drainage curve, the system is able to optimize the mobilization of fluid from the patient.

Fluid clearance can be combined with aerosol delivery to provide another therapeutic regimen. Since the inhalation of the aerosol will essentially modify the impedance characteristics of the lungs, the level of respiration and the effect of these two combined treatments can also be optimized. For example, during treatment, tidal volumes and characteristic inhalation and expulsion curves can be monitored before, during, and after treatment to ensure proper outcome of properly cleared lungs. is there.

FIG. 44 illustrates an embodiment of the present invention in which the impedance measuring device is in data communication with the cuff assist device. The system preferably includes a sensor for obtaining a physiological bioelectrical impedance signal from the patient and is operably connected to the computing device. The computing device preferably analyzes the physiological bioelectrical impedance signal and provides an output of the patient's minute ventilation and tidal volume assessment based on the analyzed bioelectrical impedance signal. The computing device also preferably monitors the signal over time and provides the signal to the cuff assist device.

The cuff assist device is preferably a non-invasive therapy that stimulates a cough to clear secretions in patients with weakened peak cough flow. It is designed to keep the lungs clear of mucus. The retained secretions collect in the lungs, creating an environment for infections. Mechanical cough assisted (MI/E) therapy products are important for patients with weakened cough who are unable to clear secretions from the large airways without assistance. The system shifts rapidly to provide positive pressure (inspiration) to inflate the lungs, and then negative pressure (exhalation), during which the secretions are sheared, It can be exhaled by cough or removed by suction. After exhalation, the system rests and maintains a static positive pressure flow in the patient. Face masks or mouthpieces can be used in endotracheal and tracheotomy (ie, for patients with appropriate adapters).

Preferably, the cuff assist device is based on the levels of tidal volume, minute ventilation, and respiration rate during therapy, characteristic therapy levels (frequency, intensity, therapy length, inspiration pressure, exhalation pressure). Pressure) is automatically adjusted. In addition, intra-session general and session-by-session lung performance is tracked to establish the efficacy of therapy (before, during, and after many sessions, and across many sessions). Can be done. Graphs may be provided to provide for recording the respiratory characteristics of the patient and to demonstrate improvement of the patient over time.

In addition, the characteristic shape of the bioimpedance expansion curve is an indicator of the success of each individual cough. Appropriate curves for maximizing exudate removal can be identified, and the cuff assist system's adjustment levels (frequency, intensity, therapy length, inspiratory pressure, and exhaled pressure) to achieve the desired cough-exhaust curve. Can be adjusted to. The cuff assist properties can be adjusted to ensure that optimal results are provided to each individual patient.

Other embodiments and technical advantages of the invention are set forth below and may be apparent from the following drawings and description of the invention or may be learned from practice of the invention.

Other embodiments and uses of the invention will be apparent to those skilled in the art from consideration of the specifications and practices of the invention disclosed herein. All references cited herein, including all publications, US and foreign patents and patent applications, are specifically and entirely incorporated by reference. The term "comprising", when used, is intended to include the terms "consisting of" and "consisting essentially of". .. Moreover, the terms "comprising," "including," and "including, containing, containing" are not intended to be limiting. It is intended that the specification and examples be considered exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

Claims (22)

A ventilation therapy system,
Computing device,
Including a plurality of sensors operatively connected to the computing device for obtaining a physiological bioelectrical impedance signal from the patient,
Computing device
Receives a physiological bioelectrical impedance signal from the sensor,
Analyze physiological bioelectrical impedance signals,
Monitor the patient's respiratory status before and/or after extubation based on the analyzed physiological bioelectrical impedance signal;
A ventilation therapy system that provides audible or visual recommendations for additional respiratory therapy or medications, or provides an indication that respiratory therapy is no longer needed, based on the patient's respiratory status. The computing device further performs real-time analysis of the shape of the expiratory and inspiratory impedance or tidal volume signal curves to prepare for extubation, the need for intubation, the need for re-intubation, and additional The ventilation therapy system according to claim 1, wherein at least one of the needs of specific treatment is determined. The treatment according to claim 1, wherein the treatment is at least one of transfer from mechanical ventilation, continuous positive pressure breathing therapy (“CPAP”), bilevel positive airway pressure (“BiPAP”), or high flow 02. Ventilation therapy system as described. 10. The ventilation therapy system of claim 1, wherein the system is adapted to provide an extubation trial prior to actual extubation while monitoring data to support extubation. The ventilation therapy system of claim 1, wherein the computing device further monitors lung performance from session to session to determine efficacy of therapy. The ventilation therapy system of claim 1, wherein the computing device provides an indication of the need to intubate or re-intubate the patient. The computing device further provides real-time feedback and control of the ventilator to account for damage to the lung from alveolar overinflation resulting from either mechanical ventilation (VILI) or natural ventilation (SILI). The ventilation therapy system of claim 1, which prevents or prevents damage through excessive driving pressure. The ventilation therapy system of claim 1, wherein a plurality of sensors are mounted on the patient's torso and the physiological bioelectrical impedance signal is measured transthoracically. The computing device further performs a real-time analysis of the flow volume loop to prepare for extubation, the need for intubation, the need for reintubation, the need for additional therapy. The ventilation therapy system according to claim 1, wherein The computing device further provides real-time feedback to prevent damage to the lungs from alveolar hyperinflation resulting from either mechanical ventilation (VILI) or natural ventilation (SILI), or The ventilation therapy system of claim 1, wherein the ventilation therapy system prevents damage through excessive driving pressure. The ventilation therapy system of claim 1, wherein the computing device further provides real-time feedback identifying collapse or closure of the lung, where the alveoli have little or no volume. A method of providing ventilation therapy, comprising:
Coupling a plurality of sensors to the patient to obtain a physiological bioelectrical impedance signal;
Coupling a plurality of sensors to a computing device,
Computing device
Receives a physiological bioelectrical impedance signal from the sensor,
Analyze physiological bioelectrical impedance signals,
Monitor the patient's respiratory status before and/or after extubation based on the analyzed physiological bioelectrical impedance signal;
A method of providing audible or visual recommendations for additional respiratory treatments or medications based on a patient's respiratory status. The computing device further performs real-time analysis of the shape of the expiratory and inspiratory impedance or tidal volume signal curves to prepare for extubation, the need for intubation, the need for re-intubation, and additional 13. The method of claim 12, wherein at least one of the need for specific treatment is determined. The additional respiratory treatment is at least one of transfer from mechanical ventilation, continuous positive airway pressure therapy (“CPAP”), bilevel positive airway pressure (“BiPAP”), or high flow 02. The method according to claim 12. 13. The method of claim 12, further comprising the step of providing an extubation trial prior to the actual extubation while monitoring the data to support the extubation. 13. The method of claim 12, wherein the computing device further monitors lung performance from session to session to determine efficacy of therapy. 13. The method of claim 12, wherein the computing device provides an indication of the need to intubate or re-intubate the patient. A computing device further provides real-time feedback and control of the ventilator to account for damage to the lung from alveolar overinflation resulting from either mechanical ventilation (VILI) or natural ventilation (SILI). 13. The method of claim 12, which prevents or prevents damage through excessive drive pressure. 13. The method of claim 12, wherein a plurality of sensors are placed on the patient's torso and the physiological bioelectrical impedance signal is measured transthoracically. The computing device further performs a real-time analysis of the flow volume loop to prepare for extubation, need for intubation, need for re-intubation, need for additional treatment. 13. The method according to claim 12, wherein The computing device further provides real-time feedback to prevent damage to the lungs from alveolar hyperinflation resulting from either mechanical ventilation (VILI) or natural ventilation (SILI), or 13. The method of claim 12, wherein damage is prevented through excessive drive pressure. 13. The method of claim 12, wherein the computing device further provides real-time feedback identifying a collapse or closure of the lung, where the alveoli have little or no volume.

Images